{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "892a35dd",
   "metadata": {},
   "source": [
    "<a href=\"https://colab.research.google.com/github/timeseriesAI/tsai/blob/master/tutorial_nbs/15_PatchTST_a_new_transformer_for_LTSF.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb178fc1",
   "metadata": {},
   "source": [
    "created by Ignacio Oguiza - email: oguiza@timeseriesAI.co"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ad9e3ba",
   "metadata": {},
   "source": [
    "# Purpose 😇"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04c53f5c",
   "metadata": {},
   "source": [
    "In this notebook, we are going to learn how to use a new state-of-the-art time series transformer called **PatchTST** to create a long-term multivariate time series forecast (LTSF). PatchTST was introduced in the following paper:\n",
    "\n",
    "* paper: Nie, Y., Nguyen, N. H., Sinthong, P., & Kalagnanam, J. (2022). **A Time Series is Worth 64 Words: Long-term Forecasting with Transformers**. arXiv preprint arXiv:2211.14730.\n",
    "* arxiv link: https://arxiv.org/abs/2211.14730\n",
    "* official repository: https://github.com/yuqinie98/PatchTST\n",
    "\n",
    "The paper will be presented at the **ICLR 2023** Conference later this year. Here's the summary of the paper review by ICLR reviewers:\n",
    "\n",
    "\"The paper applies transformer to long term forecasting problems of multi-dimensional time series. The method is very simple: Take channels independently, break them into patches and predict the patches into the future using the transformer. The main advantage of this paper is that previous papers have applied transformers to this problem but it resulted in a very weak performance, being beaten by a simple linear methods. This paper found a way to apply the transformer successfully, beating the previous methods.\"\n",
    "\n",
    "I'd like to thank the authors for publishing this paper, and for making their code available.\n",
    "\n",
    "Below you can see the results of publised in the paper."
   ]
  },
  {
   "attachments": {
    "download.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "932aa9e4",
   "metadata": {},
   "source": [
    "![download.png](attachment:download.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15YXsBIWY_Wh",
   "metadata": {},
   "source": [
    "# Install & import libraries 📚"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de64dfad",
   "metadata": {},
   "source": [
    "You'll need tsai >= 0.3.5 to be able to run this tutorial."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "O2QJUVs1xuuu",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Running command git clone --filter=blob:none --quiet https://github.com/timeseriesAI/tsai.git /tmp/pip-req-build-ex6o8mf1\n"
     ]
    }
   ],
   "source": [
    "# # **************** UNCOMMENT AND RUN THIS CELL IF YOU NEED TO INSTALL/ UPGRADE TSAI ****************\n",
    "# stable = True # Set to True for latest pip version or False for main branch in GitHub\n",
    "# !pip install {\"tsai -U\" if stable else \"git+https://github.com/timeseriesAI/tsai.git\"} >> /dev/null"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "UEEuRw7Cxuuy",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "os              : Linux-5.10.147+-x86_64-with-glibc2.29\n",
      "python          : 3.8.10\n",
      "tsai            : 0.3.5\n",
      "fastai          : 2.7.11\n",
      "fastcore        : 1.5.28\n",
      "sklearn         : 1.0.2\n",
      "torch           : 1.13.1+cu116\n",
      "device          : 1 gpu (['Tesla T4'])\n",
      "cpu cores       : 1\n",
      "threads per cpu : 2\n",
      "RAM             : 12.68 GB\n",
      "GPU memory      : [15.0] GB\n"
     ]
    }
   ],
   "source": [
    "import sklearn\n",
    "from tsai.basics import *\n",
    "my_setup(sklearn)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1E_gDwsbZOyD",
   "metadata": {},
   "source": [
    "# Load and prepare data 🔢"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f817c67f",
   "metadata": {},
   "source": [
    "The starting point for this tutorial will be a dataframe that contains our long-term time series data. \n",
    "\n",
    "`tsai` allows you to easily download and prepare data from 9 popular datasets, including weather, traffic, electricity, exchange rate, ILI, and four ETT datasets (ETTh1, ETTh2, ETTm1, ETTm2). These datasets have been extensively utilized for long-term time series forecasting benchmarking.\n",
    "\n",
    "You can download all data from here: https://cloud.tsinghua.edu.cn/d/e1ccfff39ad541908bae/\n",
    "\n",
    "Here are the statistics of these datasets:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11ae2e95",
   "metadata": {},
   "source": [
    "| Datasets | Weather | Traffic | Electricity | Exchange | ILI | ETTh1 | ETTh2 | ETTm1 | ETTm2 |\n",
    "| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n",
    "| Features | 21 | 862 | 321 | 8 | 7 | 7 | 7 | 7 | 7 |\n",
    "| Timesteps | 52696 | 17544 | 26304 | 7588 | 966 | 17420 | 17420 | 69680 | 69680 |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1bac0e18",
   "metadata": {},
   "source": [
    "## Data preparation steps"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7e204f1",
   "metadata": {},
   "source": [
    "There are 5 steps required to prepare data for a forecasting task in `tsai`:\n",
    "\n",
    "1. Prepare a dataframe with your data, including the variable you want to predict. \n",
    "2. Preprocess your data.\n",
    "3. Define train, valid and test splits.\n",
    "4. Scale your data using the train split. \n",
    "5. Apply a sliding window to prepare your input and output data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63857ba2",
   "metadata": {},
   "source": [
    "### Prepare dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a995be4b",
   "metadata": {},
   "source": [
    "In this case, we are going to download the dataframe using get_long_term_forecasting_data. You can use any of the following datasets: \"ETTh1\", \"ETTh2\", \"ETTm1\", \"ETTm2\", \"electricity\", \"exchange_rate\", \"traffic\", \"weather\", or \"ILI\".\n",
    "\n",
    "We are going to use a small dataset called ILI. ILI includes the weekly recorded **influenza-like illness (ILI)** patients data from Centers for\n",
    "Disease Control and Prevention of the United States between 2002 and 2021, which describes the ratio of patients seen with ILI and the total number of the patients.\n",
    "\n",
    "The task is a multivariate long-term time series forecasting (LTSF), where multiple variables are predicted simultaneously for multiple time steps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "be29bf2b",
   "metadata": {},
   "outputs": [
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       "          date  % WEIGHTED ILI  %UNWEIGHTED ILI  AGE 0-4  AGE 5-24  ILITOTAL  \\\n",
       "0   2002-01-01        1.222620         1.166680      582       805      2060   \n",
       "1   2002-01-08        1.333440         1.216500      683       872      2267   \n",
       "2   2002-01-15        1.319290         1.130570      642       878      2176   \n",
       "3   2002-01-22        1.494840         1.252460      728      1045      2599   \n",
       "4   2002-01-29        1.471950         1.302370      823      1189      2907   \n",
       "..         ...             ...              ...      ...       ...       ...   \n",
       "961 2020-06-02        0.839059         0.846722     2756      3528     12913   \n",
       "962 2020-06-09        0.895958         0.908885     3203      3778     13979   \n",
       "963 2020-06-16        0.910926         0.941625     3478      3796     14389   \n",
       "964 2020-06-23        0.946945         0.972185     3734      3818     14999   \n",
       "965 2020-06-30        0.963716         1.013760     3955      3843     15307   \n",
       "\n",
       "     NUM. OF PROVIDERS       OT  \n",
       "0                  754   176569  \n",
       "1                  785   186355  \n",
       "2                  831   192469  \n",
       "3                  863   207512  \n",
       "4                  909   223208  \n",
       "..                 ...      ...  \n",
       "961               3258  1525058  \n",
       "962               3254  1538038  \n",
       "963               3177  1528103  \n",
       "964               3066  1542813  \n",
       "965               3027  1509928  \n",
       "\n",
       "[966 rows x 8 columns]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dsid = \"ILI\"\n",
    "df_raw = get_long_term_forecasting_data(dsid)\n",
    "df_raw"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4710659c",
   "metadata": {},
   "source": [
    "### Proprocess dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fd41938",
   "metadata": {},
   "source": [
    "`tsai` provides some sklearn-style transforms that can be used to build a preprocessing pipeline. In this case we'll use the following transforms: \n",
    "\n",
    "* TSShrinkDataFrame: to save some memory and set the right dtypes.\n",
    "* TSDropDuplicates: to ensure there are no duplicate timestamps.\n",
    "* TSAddMissingTimestamps: to fill any missing timestamps. \n",
    "* TSFillMissing: to fill any missing data (forward fill, then 0).\n",
    "\n",
    "All these transforms can be applied to the entire dataset. In other words, they are not dependent on the training set. Other transforms will be applied later, when the training split is available.\n",
    "\n",
    "You can read about all available transforms in the [docs](https://timeseriesai.github.io/tsai/data.preprocessing.html#sklearn-api-transforms)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d20391f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data directory already exists.\n",
      "Pipeline saved as data/preproc_pipe.pkl\n",
      "Initial memory usage: 67.92 KB  \n",
      "Final memory usage  : 37.73 KB   (-44.4%)\n",
      "[Pipeline] .......... (step 1 of 4) Processing shrinker, total=   0.0s\n",
      "[Pipeline] ... (step 2 of 4) Processing drop_duplicates, total=   0.0s\n",
      "[Pipeline] ........... (step 3 of 4) Processing add_mts, total=   0.0s\n",
      "[Pipeline] ...... (step 4 of 4) Processing fill_missing, total=   0.0s\n"
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       "      <td>878</td>\n",
       "      <td>2176</td>\n",
       "      <td>831</td>\n",
       "      <td>192469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2002-01-22</td>\n",
       "      <td>1.494840</td>\n",
       "      <td>1.252460</td>\n",
       "      <td>728</td>\n",
       "      <td>1045</td>\n",
       "      <td>2599</td>\n",
       "      <td>863</td>\n",
       "      <td>207512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2002-01-29</td>\n",
       "      <td>1.471950</td>\n",
       "      <td>1.302370</td>\n",
       "      <td>823</td>\n",
       "      <td>1189</td>\n",
       "      <td>2907</td>\n",
       "      <td>909</td>\n",
       "      <td>223208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>961</th>\n",
       "      <td>2020-06-02</td>\n",
       "      <td>0.839059</td>\n",
       "      <td>0.846722</td>\n",
       "      <td>2756</td>\n",
       "      <td>3528</td>\n",
       "      <td>12913</td>\n",
       "      <td>3258</td>\n",
       "      <td>1525058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>962</th>\n",
       "      <td>2020-06-09</td>\n",
       "      <td>0.895958</td>\n",
       "      <td>0.908885</td>\n",
       "      <td>3203</td>\n",
       "      <td>3778</td>\n",
       "      <td>13979</td>\n",
       "      <td>3254</td>\n",
       "      <td>1538038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>963</th>\n",
       "      <td>2020-06-16</td>\n",
       "      <td>0.910926</td>\n",
       "      <td>0.941625</td>\n",
       "      <td>3478</td>\n",
       "      <td>3796</td>\n",
       "      <td>14389</td>\n",
       "      <td>3177</td>\n",
       "      <td>1528103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>964</th>\n",
       "      <td>2020-06-23</td>\n",
       "      <td>0.946945</td>\n",
       "      <td>0.972185</td>\n",
       "      <td>3734</td>\n",
       "      <td>3818</td>\n",
       "      <td>14999</td>\n",
       "      <td>3066</td>\n",
       "      <td>1542813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>965</th>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>0.963716</td>\n",
       "      <td>1.013760</td>\n",
       "      <td>3955</td>\n",
       "      <td>3843</td>\n",
       "      <td>15307</td>\n",
       "      <td>3027</td>\n",
       "      <td>1509928</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>966 rows × 8 columns</p>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1fa3cdfb-d28f-4ec7-ada4-ba4c71057359')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
       "              style=\"display:none;\">\n",
       "        \n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "       width=\"24px\">\n",
       "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
       "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-1fa3cdfb-d28f-4ec7-ada4-ba4c71057359 button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-1fa3cdfb-d28f-4ec7-ada4-ba4c71057359');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "          date  % WEIGHTED ILI  %UNWEIGHTED ILI  AGE 0-4  AGE 5-24  ILITOTAL  \\\n",
       "0   2002-01-01        1.222620         1.166680      582       805      2060   \n",
       "1   2002-01-08        1.333440         1.216500      683       872      2267   \n",
       "2   2002-01-15        1.319290         1.130570      642       878      2176   \n",
       "3   2002-01-22        1.494840         1.252460      728      1045      2599   \n",
       "4   2002-01-29        1.471950         1.302370      823      1189      2907   \n",
       "..         ...             ...              ...      ...       ...       ...   \n",
       "961 2020-06-02        0.839059         0.846722     2756      3528     12913   \n",
       "962 2020-06-09        0.895958         0.908885     3203      3778     13979   \n",
       "963 2020-06-16        0.910926         0.941625     3478      3796     14389   \n",
       "964 2020-06-23        0.946945         0.972185     3734      3818     14999   \n",
       "965 2020-06-30        0.963716         1.013760     3955      3843     15307   \n",
       "\n",
       "     NUM. OF PROVIDERS       OT  \n",
       "0                  754   176569  \n",
       "1                  785   186355  \n",
       "2                  831   192469  \n",
       "3                  863   207512  \n",
       "4                  909   223208  \n",
       "..                 ...      ...  \n",
       "961               3258  1525058  \n",
       "962               3254  1538038  \n",
       "963               3177  1528103  \n",
       "964               3066  1542813  \n",
       "965               3027  1509928  \n",
       "\n",
       "[966 rows x 8 columns]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datetime_col = \"date\"\n",
    "freq = '7D'\n",
    "columns = df_raw.columns[1:]\n",
    "method = 'ffill'\n",
    "value = 0\n",
    "\n",
    "# pipeline\n",
    "preproc_pipe = sklearn.pipeline.Pipeline([\n",
    "    ('shrinker', TSShrinkDataFrame()), # shrink dataframe memory usage\n",
    "    ('drop_duplicates', TSDropDuplicates(datetime_col=datetime_col)), # drop duplicate rows (if any)\n",
    "    ('add_mts', TSAddMissingTimestamps(datetime_col=datetime_col, freq=freq)), # ass missing timestamps (if any)\n",
    "    ('fill_missing', TSFillMissing(columns=columns, method=method, value=value)), # fill missing data (1st ffill. 2nd value=0)\n",
    "    ], \n",
    "    verbose=True)\n",
    "mkdir('data', exist_ok=True, parents=True)\n",
    "save_object(preproc_pipe, 'data/preproc_pipe.pkl')\n",
    "preproc_pipe = load_object('data/preproc_pipe.pkl')\n",
    "\n",
    "df = preproc_pipe.fit_transform(df_raw)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "634aa245",
   "metadata": {},
   "source": [
    "### Define splits"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5563e894",
   "metadata": {},
   "source": [
    "So we have transformed a multivariate time series with 966 time steps and 7 features (excluding the datetime) into:\n",
    "\n",
    "* 803 input samples, with 7 features and 104 historical time steps\n",
    "* 803 input samples, with 7 features and 60 future time steps."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85ca02e5",
   "metadata": {},
   "source": [
    "It's very easy to create time forecasting splits in `tsai`. You can use as function called `get_forecasting_splits`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a2abfa24",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1152x36 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "((#480) [0,1,2,3,4,5,6,7,8,9...],\n",
       " (#68) [539,540,541,542,543,544,545,546,547,548...],\n",
       " (#137) [666,667,668,669,670,671,672,673,674,675...])"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fcst_history = 104 # # steps in the past\n",
    "fcst_horizon = 60  # # steps in the future\n",
    "valid_size   = 0.1  # int or float indicating the size of the training set\n",
    "test_size    = 0.2  # int or float indicating the size of the test set\n",
    "\n",
    "splits = get_forecasting_splits(df, fcst_history=fcst_history, fcst_horizon=fcst_horizon, datetime_col=datetime_col,\n",
    "                                valid_size=valid_size, test_size=test_size)\n",
    "splits"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25d9ae42",
   "metadata": {},
   "source": [
    "However, in this example, we are going to apply the same splits they used in the original paper. You can use `get_forecasting splits` to use them. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0c84bbaa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1152x36 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "((#513) [0,1,2,3,4,5,6,7,8,9...],\n",
       " (#38) [572,573,574,575,576,577,578,579,580,581...],\n",
       " (#134) [669,670,671,672,673,674,675,676,677,678...])"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "splits = get_long_term_forecasting_splits(df, fcst_history=fcst_history, fcst_horizon=fcst_horizon, dsid=dsid)\n",
    "splits"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5951e4a0",
   "metadata": {},
   "source": [
    "### Scale dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af063b57",
   "metadata": {},
   "source": [
    "Now that we have defined the splits for this particular experiment, we'll scale the data: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c6f9c69e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data directory already exists.\n",
      "Pipeline saved as data/exp_pipe.pkl\n",
      "[Pipeline] ............ (step 1 of 1) Processing scaler, total=   0.0s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-3240d250-868e-45b8-b8cb-6411800833b2\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>% WEIGHTED ILI</th>\n",
       "      <th>%UNWEIGHTED ILI</th>\n",
       "      <th>AGE 0-4</th>\n",
       "      <th>AGE 5-24</th>\n",
       "      <th>ILITOTAL</th>\n",
       "      <th>NUM. OF PROVIDERS</th>\n",
       "      <th>OT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2002-01-01</td>\n",
       "      <td>-0.388910</td>\n",
       "      <td>-0.435545</td>\n",
       "      <td>-0.868236</td>\n",
       "      <td>-0.578640</td>\n",
       "      <td>-0.709860</td>\n",
       "      <td>-0.924247</td>\n",
       "      <td>-1.159122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2002-01-08</td>\n",
       "      <td>-0.299851</td>\n",
       "      <td>-0.392230</td>\n",
       "      <td>-0.818759</td>\n",
       "      <td>-0.563340</td>\n",
       "      <td>-0.685642</td>\n",
       "      <td>-0.859891</td>\n",
       "      <td>-1.113758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2002-01-15</td>\n",
       "      <td>-0.311222</td>\n",
       "      <td>-0.466939</td>\n",
       "      <td>-0.838844</td>\n",
       "      <td>-0.561970</td>\n",
       "      <td>-0.696289</td>\n",
       "      <td>-0.764394</td>\n",
       "      <td>-1.085416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2002-01-22</td>\n",
       "      <td>-0.170144</td>\n",
       "      <td>-0.360966</td>\n",
       "      <td>-0.796715</td>\n",
       "      <td>-0.523832</td>\n",
       "      <td>-0.646799</td>\n",
       "      <td>-0.697961</td>\n",
       "      <td>-1.015684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2002-01-29</td>\n",
       "      <td>-0.188539</td>\n",
       "      <td>-0.317573</td>\n",
       "      <td>-0.750177</td>\n",
       "      <td>-0.490947</td>\n",
       "      <td>-0.610764</td>\n",
       "      <td>-0.602464</td>\n",
       "      <td>-0.942924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>961</th>\n",
       "      <td>2020-06-02</td>\n",
       "      <td>-0.697154</td>\n",
       "      <td>-0.713722</td>\n",
       "      <td>0.196745</td>\n",
       "      <td>0.043204</td>\n",
       "      <td>0.559907</td>\n",
       "      <td>4.274116</td>\n",
       "      <td>5.091879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>962</th>\n",
       "      <td>2020-06-09</td>\n",
       "      <td>-0.651428</td>\n",
       "      <td>-0.659677</td>\n",
       "      <td>0.415718</td>\n",
       "      <td>0.100296</td>\n",
       "      <td>0.684625</td>\n",
       "      <td>4.265812</td>\n",
       "      <td>5.152048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>963</th>\n",
       "      <td>2020-06-16</td>\n",
       "      <td>-0.639399</td>\n",
       "      <td>-0.631212</td>\n",
       "      <td>0.550432</td>\n",
       "      <td>0.104406</td>\n",
       "      <td>0.732594</td>\n",
       "      <td>4.105958</td>\n",
       "      <td>5.105994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>964</th>\n",
       "      <td>2020-06-23</td>\n",
       "      <td>-0.610453</td>\n",
       "      <td>-0.604642</td>\n",
       "      <td>0.675839</td>\n",
       "      <td>0.109430</td>\n",
       "      <td>0.803962</td>\n",
       "      <td>3.875520</td>\n",
       "      <td>5.174183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>965</th>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>-0.596975</td>\n",
       "      <td>-0.568496</td>\n",
       "      <td>0.784101</td>\n",
       "      <td>0.115139</td>\n",
       "      <td>0.839997</td>\n",
       "      <td>3.794555</td>\n",
       "      <td>5.021743</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>966 rows × 8 columns</p>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3240d250-868e-45b8-b8cb-6411800833b2')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
       "              style=\"display:none;\">\n",
       "        \n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "       width=\"24px\">\n",
       "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
       "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-3240d250-868e-45b8-b8cb-6411800833b2 button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-3240d250-868e-45b8-b8cb-6411800833b2');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "          date  % WEIGHTED ILI  %UNWEIGHTED ILI   AGE 0-4  AGE 5-24  ILITOTAL  \\\n",
       "0   2002-01-01       -0.388910        -0.435545 -0.868236 -0.578640 -0.709860   \n",
       "1   2002-01-08       -0.299851        -0.392230 -0.818759 -0.563340 -0.685642   \n",
       "2   2002-01-15       -0.311222        -0.466939 -0.838844 -0.561970 -0.696289   \n",
       "3   2002-01-22       -0.170144        -0.360966 -0.796715 -0.523832 -0.646799   \n",
       "4   2002-01-29       -0.188539        -0.317573 -0.750177 -0.490947 -0.610764   \n",
       "..         ...             ...              ...       ...       ...       ...   \n",
       "961 2020-06-02       -0.697154        -0.713722  0.196745  0.043204  0.559907   \n",
       "962 2020-06-09       -0.651428        -0.659677  0.415718  0.100296  0.684625   \n",
       "963 2020-06-16       -0.639399        -0.631212  0.550432  0.104406  0.732594   \n",
       "964 2020-06-23       -0.610453        -0.604642  0.675839  0.109430  0.803962   \n",
       "965 2020-06-30       -0.596975        -0.568496  0.784101  0.115139  0.839997   \n",
       "\n",
       "     NUM. OF PROVIDERS        OT  \n",
       "0            -0.924247 -1.159122  \n",
       "1            -0.859891 -1.113758  \n",
       "2            -0.764394 -1.085416  \n",
       "3            -0.697961 -1.015684  \n",
       "4            -0.602464 -0.942924  \n",
       "..                 ...       ...  \n",
       "961           4.274116  5.091879  \n",
       "962           4.265812  5.152048  \n",
       "963           4.105958  5.105994  \n",
       "964           3.875520  5.174183  \n",
       "965           3.794555  5.021743  \n",
       "\n",
       "[966 rows x 8 columns]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns = df.columns[1:]\n",
    "train_split = splits[0]\n",
    "\n",
    "# pipeline\n",
    "exp_pipe = sklearn.pipeline.Pipeline([\n",
    "    ('scaler', TSStandardScaler(columns=columns)), # standardize data using train_split\n",
    "    ], \n",
    "    verbose=True)\n",
    "save_object(exp_pipe, 'data/exp_pipe.pkl')\n",
    "exp_pipe = load_object('data/exp_pipe.pkl')\n",
    "\n",
    "df_scaled = exp_pipe.fit_transform(df, scaler__idxs=train_split)\n",
    "df_scaled"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c28be490",
   "metadata": {},
   "source": [
    "### Apply a sliding window"
   ]
  },
  {
   "attachments": {
    "text.png": {
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"
    }
   },
   "cell_type": "markdown",
   "id": "370e42dd",
   "metadata": {},
   "source": [
    "We'll approach the time series forecasting task as a supervised learning problem. Remember that `tsai` requires that both inputs and outputs have the following shape: \n",
    "\n",
    "![text.png](attachment:text.png)"
   ]
  },
  {
   "attachments": {
    "sliding_window.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "0238cd5c",
   "metadata": {},
   "source": [
    "To get those inputs and outputs we're going to use a function called `prepare_forecasting_data` that applies a sliding window along the dataframe:\n",
    "\n",
    "![sliding_window.png](attachment:sliding_window.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a3c09e9",
   "metadata": {},
   "source": [
    "To use `prepare_forecasting_data` we need to define some settings: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bfb1b974",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_vars = df.columns[1:]\n",
    "y_vars = df.columns[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1fcf6e9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((803, 7, 104), (803, 7, 60))"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X, y = prepare_forecasting_data(df, fcst_history=fcst_history, fcst_horizon=fcst_horizon, x_vars=x_vars, y_vars=y_vars)\n",
    "X.shape, y.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dab8520f",
   "metadata": {},
   "source": [
    "# Prepare the forecaster 🏋️‍♂️"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b07920f",
   "metadata": {},
   "source": [
    "Now we'll instantiate the forecaster. In `tsai` there's a class called TSForecaster. We are going to use the same settings they used in the paper. \n",
    "\n",
    "You can find ILI specific settings here: https://github.com/yuqinie98/PatchTST/blob/main/PatchTST_supervised/scripts/PatchTST/illness.sh\n",
    "\n",
    "and default model settings here: https://github.com/yuqinie98/PatchTST/blob/main/PatchTST_supervised/run_longExp.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eb0601c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "arch_config = dict(\n",
    "    n_layers=3,  # number of encoder layers\n",
    "    n_heads=4,  # number of heads\n",
    "    d_model=16,  # dimension of model\n",
    "    d_ff=128,  # dimension of fully connected network\n",
    "    attn_dropout=0.0, # dropout applied to the attention weights\n",
    "    dropout=0.3,  # dropout applied to all linear layers in the encoder except q,k&v projections\n",
    "    patch_len=24,  # length of the patch applied to the time series to create patches\n",
    "    stride=2,  # stride used when creating patches\n",
    "    padding_patch=True,  # padding_patch\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ddaf523e",
   "metadata": {},
   "outputs": [],
   "source": [
    "learn = TSForecaster(X, y, splits=splits, batch_size=16, path=\"models\", pipelines=[preproc_pipe, exp_pipe],\n",
    "                     arch=\"PatchTST\", arch_config=arch_config, metrics=[mse, mae], cbs=ShowGraph())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d35d8f15",
   "metadata": {},
   "source": [
    "☢️ This is **not good practice**, but all papers using these long-term forecasting datasets have published there data using drop_last=True in the validtion set. You should never use it in your practice. But if you want to try and replicate the results from the paper, you may want to uncomment the following line and set `learn.dls.valid.drop_last=True`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2299f2bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# learn.dls.valid.drop_last = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "61030568",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "not enough values to plot a chart\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "PatchTST (Input shape: 16 x 7 x 104)\n",
       "============================================================================\n",
       "Layer (type)         Output Shape         Param #    Trainable \n",
       "============================================================================\n",
       "                     16 x 7 x 60         \n",
       "RevIN                                     14         True      \n",
       "____________________________________________________________________________\n",
       "                     16 x 7 x 106        \n",
       "ReplicationPad1d                                               \n",
       "____________________________________________________________________________\n",
       "                     16 x 24 x 42        \n",
       "Unfold                                                         \n",
       "____________________________________________________________________________\n",
       "                     16 x 7 x 42 x 16    \n",
       "Linear                                    400        True      \n",
       "Dropout                                                        \n",
       "Linear                                    272        True      \n",
       "Linear                                    272        True      \n",
       "Linear                                    272        True      \n",
       "Dropout                                                        \n",
       "Linear                                    272        True      \n",
       "Dropout                                                        \n",
       "Dropout                                                        \n",
       "____________________________________________________________________________\n",
       "                     16 x 16 x 42        \n",
       "Transpose                                                      \n",
       "BatchNorm1d                               32         True      \n",
       "____________________________________________________________________________\n",
       "                     16 x 42 x 16        \n",
       "Transpose                                                      \n",
       "____________________________________________________________________________\n",
       "                     16 x 42 x 128       \n",
       "Linear                                    2176       True      \n",
       "GELU                                                           \n",
       "Dropout                                                        \n",
       "____________________________________________________________________________\n",
       "                     16 x 42 x 16        \n",
       "Linear                                    2064       True      \n",
       "Dropout                                                        \n",
       "____________________________________________________________________________\n",
       "                     16 x 16 x 42        \n",
       "Transpose                                                      \n",
       "BatchNorm1d                               32         True      \n",
       "____________________________________________________________________________\n",
       "                     16 x 42 x 16        \n",
       "Transpose                                                      \n",
       "Linear                                    272        True      \n",
       "Linear                                    272        True      \n",
       "Linear                                    272        True      \n",
       "Dropout                                                        \n",
       "Linear                                    272        True      \n",
       "Dropout                                                        \n",
       "Dropout                                                        \n",
       "____________________________________________________________________________\n",
       "                     16 x 16 x 42        \n",
       "Transpose                                                      \n",
       "BatchNorm1d                               32         True      \n",
       "____________________________________________________________________________\n",
       "                     16 x 42 x 16        \n",
       "Transpose                                                      \n",
       "____________________________________________________________________________\n",
       "                     16 x 42 x 128       \n",
       "Linear                                    2176       True      \n",
       "GELU                                                           \n",
       "Dropout                                                        \n",
       "____________________________________________________________________________\n",
       "                     16 x 42 x 16        \n",
       "Linear                                    2064       True      \n",
       "Dropout                                                        \n",
       "____________________________________________________________________________\n",
       "                     16 x 16 x 42        \n",
       "Transpose                                                      \n",
       "BatchNorm1d                               32         True      \n",
       "____________________________________________________________________________\n",
       "                     16 x 42 x 16        \n",
       "Transpose                                                      \n",
       "Linear                                    272        True      \n",
       "Linear                                    272        True      \n",
       "Linear                                    272        True      \n",
       "Dropout                                                        \n",
       "Linear                                    272        True      \n",
       "Dropout                                                        \n",
       "Dropout                                                        \n",
       "____________________________________________________________________________\n",
       "                     16 x 16 x 42        \n",
       "Transpose                                                      \n",
       "BatchNorm1d                               32         True      \n",
       "____________________________________________________________________________\n",
       "                     16 x 42 x 16        \n",
       "Transpose                                                      \n",
       "____________________________________________________________________________\n",
       "                     16 x 42 x 128       \n",
       "Linear                                    2176       True      \n",
       "GELU                                                           \n",
       "Dropout                                                        \n",
       "____________________________________________________________________________\n",
       "                     16 x 42 x 16        \n",
       "Linear                                    2064       True      \n",
       "Dropout                                                        \n",
       "____________________________________________________________________________\n",
       "                     16 x 16 x 42        \n",
       "Transpose                                                      \n",
       "BatchNorm1d                               32         True      \n",
       "____________________________________________________________________________\n",
       "                     16 x 42 x 16        \n",
       "Transpose                                                      \n",
       "____________________________________________________________________________\n",
       "                     16 x 7 x 672        \n",
       "Flatten                                                        \n",
       "____________________________________________________________________________\n",
       "                     16 x 7 x 60         \n",
       "Linear                                    40380      True      \n",
       "____________________________________________________________________________\n",
       "\n",
       "Total params: 56,970\n",
       "Total trainable params: 56,970\n",
       "Total non-trainable params: 0\n",
       "\n",
       "Optimizer used: <function Adam>\n",
       "Loss function: FlattenedLoss of MSELoss()\n",
       "\n",
       "Callbacks:\n",
       "  - TrainEvalCallback\n",
       "  - CastToTensor\n",
       "  - Recorder\n",
       "  - ProgressCallback\n",
       "  - ShowGraph"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "learn.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6859e9dc",
   "metadata": {},
   "source": [
    "As you can see this is a very small model, with only 57k parameters!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd8823e9",
   "metadata": {},
   "source": [
    "# Train model 🏃🏿‍♂️"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c090ef6",
   "metadata": {},
   "source": [
    "In this case we'll use the same number of epochs and learning rate they used in the paper. \n",
    "\n",
    "⚠️ Whenever you need to look for a good learning rate to train a model you can use:\n",
    "```python\n",
    "lr_max = learn.lr_find().valley\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9jzge9_a8niH",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>mse</th>\n",
       "      <th>mae</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.960463</td>\n",
       "      <td>0.318112</td>\n",
       "      <td>0.318112</td>\n",
       "      <td>0.397974</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.847100</td>\n",
       "      <td>0.232948</td>\n",
       "      <td>0.232948</td>\n",
       "      <td>0.332745</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.766477</td>\n",
       "      <td>0.249021</td>\n",
       "      <td>0.249021</td>\n",
       "      <td>0.331546</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.709651</td>\n",
       "      <td>0.251118</td>\n",
       "      <td>0.251118</td>\n",
       "      <td>0.326470</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.670353</td>\n",
       "      <td>0.250177</td>\n",
       "      <td>0.250177</td>\n",
       "      <td>0.329263</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.636711</td>\n",
       "      <td>0.276281</td>\n",
       "      <td>0.276281</td>\n",
       "      <td>0.338399</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.602480</td>\n",
       "      <td>0.267605</td>\n",
       "      <td>0.267605</td>\n",
       "      <td>0.349807</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.572344</td>\n",
       "      <td>0.305415</td>\n",
       "      <td>0.305415</td>\n",
       "      <td>0.382066</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>0.547722</td>\n",
       "      <td>0.326171</td>\n",
       "      <td>0.326171</td>\n",
       "      <td>0.395411</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0.526130</td>\n",
       "      <td>0.294327</td>\n",
       "      <td>0.294327</td>\n",
       "      <td>0.370027</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>0.503540</td>\n",
       "      <td>0.256939</td>\n",
       "      <td>0.256939</td>\n",
       "      <td>0.345768</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>0.480784</td>\n",
       "      <td>0.280642</td>\n",
       "      <td>0.280642</td>\n",
       "      <td>0.341652</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>0.466183</td>\n",
       "      <td>0.299963</td>\n",
       "      <td>0.299963</td>\n",
       "      <td>0.369141</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>0.454535</td>\n",
       "      <td>0.292823</td>\n",
       "      <td>0.292823</td>\n",
       "      <td>0.337604</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>0.425917</td>\n",
       "      <td>0.231199</td>\n",
       "      <td>0.231199</td>\n",
       "      <td>0.317651</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>0.400605</td>\n",
       "      <td>0.213708</td>\n",
       "      <td>0.213708</td>\n",
       "      <td>0.304334</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>0.375422</td>\n",
       "      <td>0.241104</td>\n",
       "      <td>0.241104</td>\n",
       "      <td>0.328602</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>0.355786</td>\n",
       "      <td>0.305646</td>\n",
       "      <td>0.305646</td>\n",
       "      <td>0.394113</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>0.339242</td>\n",
       "      <td>0.273150</td>\n",
       "      <td>0.273150</td>\n",
       "      <td>0.340469</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>0.324994</td>\n",
       "      <td>0.261849</td>\n",
       "      <td>0.261849</td>\n",
       "      <td>0.341164</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>0.305946</td>\n",
       "      <td>0.251772</td>\n",
       "      <td>0.251772</td>\n",
       "      <td>0.328020</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>0.297061</td>\n",
       "      <td>0.354647</td>\n",
       "      <td>0.354647</td>\n",
       "      <td>0.409232</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>0.278671</td>\n",
       "      <td>0.252273</td>\n",
       "      <td>0.252273</td>\n",
       "      <td>0.332556</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>0.266362</td>\n",
       "      <td>0.286465</td>\n",
       "      <td>0.286465</td>\n",
       "      <td>0.352109</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>0.267009</td>\n",
       "      <td>0.266730</td>\n",
       "      <td>0.266730</td>\n",
       "      <td>0.332993</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>0.259881</td>\n",
       "      <td>0.307498</td>\n",
       "      <td>0.307498</td>\n",
       "      <td>0.367873</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>0.251215</td>\n",
       "      <td>0.258133</td>\n",
       "      <td>0.258133</td>\n",
       "      <td>0.334108</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>0.249051</td>\n",
       "      <td>0.271655</td>\n",
       "      <td>0.271655</td>\n",
       "      <td>0.332401</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>0.243153</td>\n",
       "      <td>0.243343</td>\n",
       "      <td>0.243343</td>\n",
       "      <td>0.322043</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>0.239356</td>\n",
       "      <td>0.233354</td>\n",
       "      <td>0.233354</td>\n",
       "      <td>0.324321</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>0.233683</td>\n",
       "      <td>0.277336</td>\n",
       "      <td>0.277336</td>\n",
       "      <td>0.345235</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>0.226431</td>\n",
       "      <td>0.266492</td>\n",
       "      <td>0.266492</td>\n",
       "      <td>0.352057</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>0.225376</td>\n",
       "      <td>0.274813</td>\n",
       "      <td>0.274813</td>\n",
       "      <td>0.339011</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>0.221858</td>\n",
       "      <td>0.289266</td>\n",
       "      <td>0.289266</td>\n",
       "      <td>0.340273</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>0.221712</td>\n",
       "      <td>0.268049</td>\n",
       "      <td>0.268049</td>\n",
       "      <td>0.328669</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>0.216189</td>\n",
       "      <td>0.315708</td>\n",
       "      <td>0.315708</td>\n",
       "      <td>0.366108</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>0.211581</td>\n",
       "      <td>0.341631</td>\n",
       "      <td>0.341631</td>\n",
       "      <td>0.383725</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>0.216236</td>\n",
       "      <td>0.253907</td>\n",
       "      <td>0.253907</td>\n",
       "      <td>0.337624</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>0.209497</td>\n",
       "      <td>0.304378</td>\n",
       "      <td>0.304378</td>\n",
       "      <td>0.351922</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>0.205925</td>\n",
       "      <td>0.261737</td>\n",
       "      <td>0.261737</td>\n",
       "      <td>0.332242</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>0.200285</td>\n",
       "      <td>0.294982</td>\n",
       "      <td>0.294982</td>\n",
       "      <td>0.349143</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>0.197233</td>\n",
       "      <td>0.284772</td>\n",
       "      <td>0.284772</td>\n",
       "      <td>0.341562</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>0.196371</td>\n",
       "      <td>0.323302</td>\n",
       "      <td>0.323302</td>\n",
       "      <td>0.355856</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>0.192355</td>\n",
       "      <td>0.322229</td>\n",
       "      <td>0.322229</td>\n",
       "      <td>0.361854</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>0.186424</td>\n",
       "      <td>0.299490</td>\n",
       "      <td>0.299490</td>\n",
       "      <td>0.350974</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>0.184477</td>\n",
       "      <td>0.313175</td>\n",
       "      <td>0.313175</td>\n",
       "      <td>0.360041</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46</td>\n",
       "      <td>0.183703</td>\n",
       "      <td>0.298801</td>\n",
       "      <td>0.298801</td>\n",
       "      <td>0.346851</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>0.178037</td>\n",
       "      <td>0.310054</td>\n",
       "      <td>0.310054</td>\n",
       "      <td>0.354497</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>0.173407</td>\n",
       "      <td>0.320075</td>\n",
       "      <td>0.320075</td>\n",
       "      <td>0.362885</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>0.171585</td>\n",
       "      <td>0.300244</td>\n",
       "      <td>0.300244</td>\n",
       "      <td>0.352675</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50</td>\n",
       "      <td>0.176481</td>\n",
       "      <td>0.309094</td>\n",
       "      <td>0.309094</td>\n",
       "      <td>0.370663</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51</td>\n",
       "      <td>0.171682</td>\n",
       "      <td>0.310276</td>\n",
       "      <td>0.310276</td>\n",
       "      <td>0.359710</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52</td>\n",
       "      <td>0.167195</td>\n",
       "      <td>0.325958</td>\n",
       "      <td>0.325958</td>\n",
       "      <td>0.367347</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53</td>\n",
       "      <td>0.167835</td>\n",
       "      <td>0.364062</td>\n",
       "      <td>0.364062</td>\n",
       "      <td>0.398513</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54</td>\n",
       "      <td>0.166141</td>\n",
       "      <td>0.350721</td>\n",
       "      <td>0.350721</td>\n",
       "      <td>0.392628</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55</td>\n",
       "      <td>0.165374</td>\n",
       "      <td>0.324358</td>\n",
       "      <td>0.324358</td>\n",
       "      <td>0.360780</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56</td>\n",
       "      <td>0.165650</td>\n",
       "      <td>0.302087</td>\n",
       "      <td>0.302087</td>\n",
       "      <td>0.365833</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>57</td>\n",
       "      <td>0.163407</td>\n",
       "      <td>0.361788</td>\n",
       "      <td>0.361788</td>\n",
       "      <td>0.381106</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>58</td>\n",
       "      <td>0.159527</td>\n",
       "      <td>0.324757</td>\n",
       "      <td>0.324757</td>\n",
       "      <td>0.375798</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>59</td>\n",
       "      <td>0.155974</td>\n",
       "      <td>0.324168</td>\n",
       "      <td>0.324168</td>\n",
       "      <td>0.360353</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>60</td>\n",
       "      <td>0.158671</td>\n",
       "      <td>0.337781</td>\n",
       "      <td>0.337781</td>\n",
       "      <td>0.382119</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>61</td>\n",
       "      <td>0.155932</td>\n",
       "      <td>0.335248</td>\n",
       "      <td>0.335248</td>\n",
       "      <td>0.365414</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>62</td>\n",
       "      <td>0.155371</td>\n",
       "      <td>0.341751</td>\n",
       "      <td>0.341751</td>\n",
       "      <td>0.371250</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>63</td>\n",
       "      <td>0.153168</td>\n",
       "      <td>0.330818</td>\n",
       "      <td>0.330818</td>\n",
       "      <td>0.360270</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>64</td>\n",
       "      <td>0.149985</td>\n",
       "      <td>0.325722</td>\n",
       "      <td>0.325722</td>\n",
       "      <td>0.357438</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>65</td>\n",
       "      <td>0.149124</td>\n",
       "      <td>0.323831</td>\n",
       "      <td>0.323831</td>\n",
       "      <td>0.367716</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>66</td>\n",
       "      <td>0.145939</td>\n",
       "      <td>0.354237</td>\n",
       "      <td>0.354237</td>\n",
       "      <td>0.381599</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>67</td>\n",
       "      <td>0.145629</td>\n",
       "      <td>0.328715</td>\n",
       "      <td>0.328715</td>\n",
       "      <td>0.359400</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>68</td>\n",
       "      <td>0.142983</td>\n",
       "      <td>0.318045</td>\n",
       "      <td>0.318045</td>\n",
       "      <td>0.365613</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>69</td>\n",
       "      <td>0.142453</td>\n",
       "      <td>0.322606</td>\n",
       "      <td>0.322606</td>\n",
       "      <td>0.353600</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>70</td>\n",
       "      <td>0.142813</td>\n",
       "      <td>0.339263</td>\n",
       "      <td>0.339263</td>\n",
       "      <td>0.367234</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>71</td>\n",
       "      <td>0.139553</td>\n",
       "      <td>0.332137</td>\n",
       "      <td>0.332137</td>\n",
       "      <td>0.362777</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>72</td>\n",
       "      <td>0.137720</td>\n",
       "      <td>0.328443</td>\n",
       "      <td>0.328443</td>\n",
       "      <td>0.361607</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>73</td>\n",
       "      <td>0.136744</td>\n",
       "      <td>0.346788</td>\n",
       "      <td>0.346788</td>\n",
       "      <td>0.372799</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>74</td>\n",
       "      <td>0.135157</td>\n",
       "      <td>0.326163</td>\n",
       "      <td>0.326163</td>\n",
       "      <td>0.361193</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75</td>\n",
       "      <td>0.134545</td>\n",
       "      <td>0.345362</td>\n",
       "      <td>0.345362</td>\n",
       "      <td>0.377601</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>76</td>\n",
       "      <td>0.136142</td>\n",
       "      <td>0.335154</td>\n",
       "      <td>0.335154</td>\n",
       "      <td>0.367049</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>77</td>\n",
       "      <td>0.135106</td>\n",
       "      <td>0.333251</td>\n",
       "      <td>0.333251</td>\n",
       "      <td>0.369926</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>78</td>\n",
       "      <td>0.133691</td>\n",
       "      <td>0.343773</td>\n",
       "      <td>0.343773</td>\n",
       "      <td>0.380361</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>79</td>\n",
       "      <td>0.133394</td>\n",
       "      <td>0.344434</td>\n",
       "      <td>0.344434</td>\n",
       "      <td>0.373703</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>80</td>\n",
       "      <td>0.131893</td>\n",
       "      <td>0.342828</td>\n",
       "      <td>0.342828</td>\n",
       "      <td>0.369331</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>81</td>\n",
       "      <td>0.130908</td>\n",
       "      <td>0.341702</td>\n",
       "      <td>0.341702</td>\n",
       "      <td>0.372069</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>82</td>\n",
       "      <td>0.129816</td>\n",
       "      <td>0.338599</td>\n",
       "      <td>0.338599</td>\n",
       "      <td>0.368190</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>83</td>\n",
       "      <td>0.129442</td>\n",
       "      <td>0.355630</td>\n",
       "      <td>0.355630</td>\n",
       "      <td>0.381579</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>84</td>\n",
       "      <td>0.130848</td>\n",
       "      <td>0.332511</td>\n",
       "      <td>0.332511</td>\n",
       "      <td>0.373070</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>85</td>\n",
       "      <td>0.129371</td>\n",
       "      <td>0.341991</td>\n",
       "      <td>0.341991</td>\n",
       "      <td>0.372557</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>86</td>\n",
       "      <td>0.126832</td>\n",
       "      <td>0.345468</td>\n",
       "      <td>0.345468</td>\n",
       "      <td>0.375948</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>87</td>\n",
       "      <td>0.127005</td>\n",
       "      <td>0.342472</td>\n",
       "      <td>0.342472</td>\n",
       "      <td>0.371225</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>88</td>\n",
       "      <td>0.128173</td>\n",
       "      <td>0.348310</td>\n",
       "      <td>0.348310</td>\n",
       "      <td>0.376986</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>89</td>\n",
       "      <td>0.128793</td>\n",
       "      <td>0.343299</td>\n",
       "      <td>0.343299</td>\n",
       "      <td>0.373782</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>90</td>\n",
       "      <td>0.128462</td>\n",
       "      <td>0.340427</td>\n",
       "      <td>0.340427</td>\n",
       "      <td>0.370179</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>91</td>\n",
       "      <td>0.126489</td>\n",
       "      <td>0.346056</td>\n",
       "      <td>0.346056</td>\n",
       "      <td>0.372910</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>92</td>\n",
       "      <td>0.125805</td>\n",
       "      <td>0.345921</td>\n",
       "      <td>0.345921</td>\n",
       "      <td>0.374168</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>93</td>\n",
       "      <td>0.125276</td>\n",
       "      <td>0.345993</td>\n",
       "      <td>0.345993</td>\n",
       "      <td>0.374182</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>94</td>\n",
       "      <td>0.126957</td>\n",
       "      <td>0.347983</td>\n",
       "      <td>0.347983</td>\n",
       "      <td>0.374687</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>95</td>\n",
       "      <td>0.126483</td>\n",
       "      <td>0.352475</td>\n",
       "      <td>0.352475</td>\n",
       "      <td>0.378176</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>96</td>\n",
       "      <td>0.126410</td>\n",
       "      <td>0.345475</td>\n",
       "      <td>0.345475</td>\n",
       "      <td>0.374583</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>97</td>\n",
       "      <td>0.126375</td>\n",
       "      <td>0.345989</td>\n",
       "      <td>0.345989</td>\n",
       "      <td>0.373902</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>98</td>\n",
       "      <td>0.126412</td>\n",
       "      <td>0.342558</td>\n",
       "      <td>0.342558</td>\n",
       "      <td>0.372427</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>99</td>\n",
       "      <td>0.125798</td>\n",
       "      <td>0.345804</td>\n",
       "      <td>0.345804</td>\n",
       "      <td>0.374178</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
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       "<IPython.core.display.HTML object>"
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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 936x648 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn = TSForecaster(X, y, splits=splits, batch_size=16, path=\"models\", pipelines=[preproc_pipe, exp_pipe],\n",
    "                     arch=\"PatchTST\", arch_config=arch_config, metrics=[mse, mae], cbs=[ShowGraph()])\n",
    "\n",
    "n_epochs = 100\n",
    "lr_max = 0.0025\n",
    "learn.fit_one_cycle(n_epochs, lr_max=lr_max)\n",
    "learn.export('patchTST.pt')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c50d196",
   "metadata": {},
   "source": [
    "# Evaluate model 🕵️‍♀️"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a75e3d07",
   "metadata": {},
   "source": [
    "## Valid split"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bea2127b",
   "metadata": {},
   "source": [
    "First we are going to check that the valid predictions match the results we got during training. But you can skip this step since it's not required."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "18d22ab6",
   "metadata": {},
   "outputs": [
    {
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     "name": "stdout",
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      "scaled_preds.shape: (38, 7, 60)\n"
     ]
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       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-495e30b2-2af5-41b4-a7ac-80fa8d1c0ec0');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "            mse       mae\n",
       "valid  0.345804  0.374178"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from tsai.inference import load_learner\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
    "\n",
    "learn = load_learner('models/patchTST.pt')\n",
    "scaled_preds, *_ = learn.get_X_preds(X[splits[1]])\n",
    "scaled_preds = to_np(scaled_preds)\n",
    "print(f\"scaled_preds.shape: {scaled_preds.shape}\")\n",
    "\n",
    "scaled_y_true = y[splits[1]]\n",
    "results_df = pd.DataFrame(columns=[\"mse\", \"mae\"])\n",
    "results_df.loc[\"valid\", \"mse\"] = mean_squared_error(scaled_y_true.flatten(), scaled_preds.flatten())\n",
    "results_df.loc[\"valid\", \"mae\"] = mean_absolute_error(scaled_y_true.flatten(), scaled_preds.flatten())\n",
    "results_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "096fd320",
   "metadata": {},
   "source": [
    "## Test split"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5cf5b8d",
   "metadata": {},
   "source": [
    "So now we'll use the test split to measure performance (this is the one you that is published in the paper). \n",
    "\n",
    "⚠️ You may find some differences due to randomness of the process. In addition to that, the authors used a test dataloader that drop the last batch if incomplete, which means that not all samples are used to measure performance. In `tsai` we are using all samples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "53eda99b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y_test_preds.shape: (134, 7, 60)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-b0d6909d-1810-4ac5-8f90-2f1d23c83dcf\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mse</th>\n",
       "      <th>mae</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>test</th>\n",
       "      <td>1.534231</td>\n",
       "      <td>0.799998</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b0d6909d-1810-4ac5-8f90-2f1d23c83dcf')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
       "              style=\"display:none;\">\n",
       "        \n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "       width=\"24px\">\n",
       "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
       "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-b0d6909d-1810-4ac5-8f90-2f1d23c83dcf button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-b0d6909d-1810-4ac5-8f90-2f1d23c83dcf');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "           mse       mae\n",
       "test  1.534231  0.799998"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from tsai.inference import load_learner\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
    "\n",
    "learn = load_learner('models/patchTST.pt')\n",
    "y_test_preds, *_ = learn.get_X_preds(X[splits[2]])\n",
    "y_test_preds = to_np(y_test_preds)\n",
    "print(f\"y_test_preds.shape: {y_test_preds.shape}\")\n",
    "\n",
    "y_test = y[splits[2]]\n",
    "results_df = pd.DataFrame(columns=[\"mse\", \"mae\"])\n",
    "results_df.loc[\"test\", \"mse\"] = mean_squared_error(y_test.flatten(), y_test_preds.flatten())\n",
    "results_df.loc[\"test\", \"mae\"] = mean_absolute_error(y_test.flatten(), y_test_preds.flatten())\n",
    "results_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5669f7c",
   "metadata": {},
   "source": [
    "### Visualize predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cf7d3ff4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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+bRwXl0G8vb2JiqpnBHYLZ0miPQqMUEr5A2XAJCDerlEJIUQ9jNh9pU7RT4FuZOUn70CoKmoRiVbUz5I+2i3AYmA7sMd8TcvpeBBCOJUBA5xktaW87VDTyOIZIz+BVk6025AwhEWjjrXWf9Fa99FaR2utZ2utK+wdmBBC1MWea9JaZefjcCq54XOC+yEL8An5CxBCuJTOnTsbHYJJRTa0aqSveMNMyF7vmHiE05JEK4QQTVGeDb4NjToGvANMg6FEiyaJVgjhUjIynGRZw9hXwTuk4XO8zIOhRIsmmwoIIVzK4MGDjQ4BamtMmwV4eDZ8Xthl0Poix8QknJY80QohXMqePXuMDgFOJcEPQxo/L3ImtB9t/3iEU5NEK4RwKZ6ejTxFOkJFTuP9swCH3oP9r9o/HuHUpOlYCOFSevXqZXQIFiy/aFZdDCWpdg9HODd5ohVCuBSn2AatVXvoNKnx87wCZDCUkCdaIYRrsXZBd7voeLHpqzG+HcDL397RCCcniVYI4VIqKyuNDgGS/g2BPSDiqobPi5hu+hItmjQdCyFcSlZWltEhQF48VJc0fl7J0YY3hxctgiRaIYRLiY2NNToE06pQjS2/CFCR26I2fxd1k0QrhHApCQkJRocAusqyUcfegbIEo5A+WiGEa/Hz8zM6BLhkjWXneQVAtYw6bunkiVYI4VIiIyONDUBr2Pt845u+g+mpd5KFSVm4LUm0QgiXkpSUZGwA1UWw91lQFrx9enhCZa5lSVm4LUm0QgiXYvgTbUWOZf2zp626TBataOEk0QohXEpRkcFJq9zC5RdP8w6UftoWThKtEMKl5ObmGhtAcD8Y8YHl53sHQpWMPG7JJNEKIVyK4fNoa8rAp63l5/d9DFpZcb5wO5JohRAuxfB5tEc+gf3/tvz87rea1jwWLZYkWiGESwkICDA2AGv7aDfMhOPf2S8e4fQk0QohXEpYWJixAVTkWLb84tmqCu0Ti3AJkmiFEC4lOTnZ2ADCLoV2wy0/3ztQpve0cLIEoxDCpXTv3t3YALpeZ935wdEyGKqFkydaIYRLMXx6z08joPiI5ef3/oP1yVm4FUm0QgiXUlBQYFzltdVQsNu6UcQnfoTDH9svJuH0JNEKIVyKofNoTx0A/wjwam35NSVpkL3BfjEJpyeJVgjhUgydR1t1CiKusu4a2ZO2xZPBUEIIlxISEmJc5e1Hmr6s4SWjjls6eaIVQriU0NBQ4yrf+SQUJFp3TdilMPYr+8QjXIIkWiGES0lJSTGu8iMfg5eVK1NVl0DmL/aJR7gEixKtUipEKbVYKbVfKZWklLKy7UQIIWyjV69exlRcnmNKmq0vsvK6k7D9QfvEJFyCpU+0rwI/aq37AIOAJPuFJIQQ9Ttx4oQxFRclQ5shoJR113kFyH60LVyjg6GUUsHAOGAOgNa6Eqi0b1hCCFG34mKDRvC2HwUTV1p/nexH2+JZ8kQbBWQDHyqldiil3lNKXTCJTCk1VykVr5SKz87OtnmgQggBBs6jTfsSSqxYEeo0rwAY/p7t4xEuw5JE6wXEAG9rrYcAJcDj55+ktZ6vtY7TWse1b2/FFlJCCGEFw+bR7nsOKvKsv055QOepoGttH5NwCZYk2nQgXWu9xfz9YkyJVwghHM6Q6T21VXBqP4REN+365b2hPMu2MQmX0Wii1VpnAseUUr3NL00C9tk1KiGEqEdgYKDjKz11APy7gpd/066XRStaNEtHHf8B+FwptRsYDPzTfiEJIUT9UlNTHV9pYA8Y/23Tr/eWkcctmUVLMGqtdwJxdo5FCCEa1bdvX8dXWnQIfDs2/fqIq01PtaJFkpWhhBAuxZAn2t3/B1lrm379gKchqKft4hEuRRKtEMKllJWVOb7SkjTrV4Q6256/QcYK28UjXIrs3iOEcCmGzKNtbqItPQYl4baLR7gUeaIVQrgUh8+j1bUw4Blo1a7pZXgFymCoFkwSrRDCpXTo0MGxFWoNve61fo3js7VqJwtWtGDSdCyEcCk+Pj6OrTDjRzj4Dly8rOllRP/ZdvEIlyNPtEIIl5Kenu7YCkuPgn9Y88rI3wXHv7NNPMLlSKIVQriU6OgmLoPYVM0dCAVQsBvSFtomHuFyJNEKIVxKcnKyYyv07wJtm7lejwyGatGkj1YI4VJqamocW2Gve5tfhk8wVBY0vxzhkuSJVgjhUgYMGODYCtddC5X5zSujbSzEvWGbeITLkUQrhHApO3fudFxlNZVwfFnz1yn2bOKuP8ItSKIVQriUzp07O66y0mPg1xk8mtnLVlMKK0bYJibhciTRCiFEfcpPQqANNgPwCoSactMTsmhxJNEKIVxKRkaG4yprPwom2GAzAKXAp03z+3qFS5JEK4RwKYMHD3ZcZZm/QF68bcrq9wR4eNumLOFSJNEKIVzKnj17HFdZ6memVZ1soe+D0KqtbcoSLkUSrRDCpXh6ejquMlusCnXapltkT9oWShKtEMKl9OrVy3GVlaRB6662Kau2GsqzbFOWcCmSaIUQLiUxMdFxlY1ZDK2jbFOWDIZqsWQJRiGES4mIiHBMRdWl4OEDnjbali+oD3i1tk1ZwqXIE60QwqVUVjpoLmr+Lth8q+3K630fdLdhecJlSKIVQriUrCwH9XPmJUBgD9uVl7MFDr5ru/KEy5BEK4RwKbGxsfavpKoI9v0T+jxouzLLM+H4ctuVJ1yGJFohhEtJSEiwfyWefjDyMwht5j60Z/NpA1UyGKolksFQQgiX4ufnZ98KilMhfwd0udq25cqo4xZLnmiFEC4lMjLSvhXsfAwK7LD6VFBfuGSd7csVTk8SrRDCpSQlJdmv8LwdkL0e+j5s+7KVJ2Sttn25wulJohVCuBS7PtEeXQR9HgAvO23UvmEmVJfZp2zhtKSPVgjhUoqKiuxX+KDnQFfbp+yzt8rzsnM/s3Aq8kQrhHApubm59ik48VnI2WTfrexkQFSLZHGiVUp5KqV2KKVkIpgQwjB2mUdbng37X4aA7rYv+2yDXwDfjvatQzgda55o7wfsOApBCOdSmlvKgWUHjA5DnMcu82iTX4eu14OfnZNg2GXgE2zfOoTTsSjRKqUigMuB9+wbjhDO48A3B/hyxpdk78s2OhRxloCAANsXWpYBPefZvtzzbb4NUj+3fz3CqVj6RPsK8ChQW98JSqm5Sql4pVR8dra8MQnXd3L3SUJ7hbL87uVorY0OR5iFhYXZvtDh/4U2A21f7vmkj7ZFajTRKqWuALK01g2212it52ut47TWce3bt7dZgEIY5eTuk0x+cTLVZdXs+niX0eEIs+TkZNsWWFMBm+bYtsz6SKJtkSx5oh0NTFdKpQILgYlKqc/sGpUQBtNac3L3SToP6czl71zOysdWUppbanRYAuje3cYDlsoy4OQq25ZZn7axEGDDHYGES2g00Wqtn9BaR2itI4EbgV+11rPsHpkQBirOKEZ5KFp3bE1YbBiDbhnEZ1M/o/BoodGhtXg2n95TdgL87NAcDVSXV1Ndfta83Igrodvv7VKXcF4yj1aIOpzcfZKOAzuilALgkhcuIfrGaN4b/h5HVh0xOLqWraCgwLYFlp0Af9sm2uKTxaRvTmf98+v59o5vf+vjz42HHY/YtC7h/KxKtFrr1VrrK+wVjBDO4nSiPU0pxaiHR3H1Z1fz5YwvyT8i/WxGsfk82i4zTFvi2Ujh0UI+HPshR1YdYfSjo8nZn8OGFzaYDtZWQtZ6m9UlXIM80QpRh/MT7WndJnUjLC6M3AN2Wp1INMrm82hzNkFJms2KW3LTEobcPoSxT4zF29+bG7+5ka1vbCXnQI7sSdtCSaIVog71JVqA4K7BFB6TvlqjhISE2LbAg+9A7habFFVdXo1/e39GPjjyzGtB4UHMS5xHu97twKct1FbZpC7hOiTRCnGemsoa8g7m0b5f3dPUgroEyaAoA4WGhtq2QBsOhvLy9eLGr2/E09vznNd9Q3zZ9PImjiZUwPQUm9QlXIckWiHOk7M/h5CoELx8697cKrhrMKeOnXJwVOK0lBQbJyobJtoFVywg71Bencc8vDyIfyceDrwmW+W5g5pyqKm06FRJtEKcp6FmY4DgLsHyRGugXr162bbA2FcgIKrZxeQezOVE/AlCoupu2h5w8wCSlydTtvUVqJDV81xe6gLYcrtFp0qiFeI8jSbarpJojXTixAnbFVZbDSEDbbLR+95Fe+l3bT88POt+W/UP9afvjL5kHI2S1aHcQc4mCB1u0amSaIU4T2OJNigiiFPpp9C1sv6xEYqLi21Y2BH4ebRNiqosrmTAzAENnnPV+1fRbXi1JFp34B0MHcZZdGrdnVBCtGCNJVpvf29aBbaiJLuEgI522ElGNMim82ht2D97yfOXWHTeT/+by8CgMDrLtrSuLeYli0+VJ1ohzlJ0ooiaihqCugQ1eF5QlyAZEGUQm86jtVGi3fTyJnZ9YtnGEz7BQez6dF+z6xQGyloLu56y+HRJtEKcJX1LOhEjIs4svVgf6ac1jk2n9/hHQMRVzS4mcWEiQRENfzg7LTpuM/u+3CVdD67s5CrQls+HlkQrxFnSN6cTPiK80fOCugTJohUGCQwMtF1hHcZC5MxmFVGcWUzewTy6ju1q0fnth/SlQ2QxBWk2XrNZOE7OJmg3svHzzCTRCnGW45uPEzEiotHz5InWOKmpqbYrLOEBOPFTs4rIO5THgFkDLlikol7B/Zj1l59oE9WmWfUKA5VlSKIVoilqq2vJ2J5B+LDGn2iDu8iiFUbp27ev7QrL3w6ePs0qouuYrkx7fZrlFwT1oarjzSy6ehG1NbXNqlsYZNou8O1g8emSaIUwy0rMIqhLEL7Bvo2eK0+0xrHpE21p8wZDVZdXs/CqhdRWW5Ewvfzxjn2MgrQC0tbYbjMD4SAZP0P6t1ZdIolWCLP0zekWNRuDjDo2UlmZDZcv9A4Av85Nvjx1TSqluaV4eFn5Vrrx9/SfComLEptctzDI0UVQesyqSyTRCmFmTaIN7BxISXYJNZU1do5KnM+m82gv2wHelo0Wrkvy8mR6XdGEJSH9w4i+OJu8g3WviyycmJUDoUASrRBnWJNoPbw8COgUQNGJIjtHJc5ns3m0pemw78VmFZF3MK9piTaoHyEB+7jl11uaVb9wsMoCKDkGIQ2vAHY+SbRCAGV5ZRSdKKJ9/7q3xquL9NMao0MHywehNOjUATjxfbOKmPXjLDpENyGetjHg24mjG46y7rl1zYpBOJBPCPzuKHh4W3WZJFohgONbjxMWF1bvgvB1Ce4iG8AbwceneaOEz2jmqlDx78Zz+JfDTbs4JBpiXyagYwBbXt0io49dRcYKqMi1+jJJtEJgWhEqfHjj03rOFtRVNoA3Qnp6um0KKjsB/k1LtFprNv17E62CWjW9/q330LZTIQEdAzi20brBNcIgu5+CsuNWXyaJVgjgyC9H6DKqi1XXyFxaY0RHR9umoJ73Qt9HmnTpyV0nqa2uJSyuGesklx6Fgt30u74fmTszm16OcIyqYijcC6HDrL5UEm0dZA3SluVEwgkKUgvocWkPq66TPlpjJCcn26agnE3g1bpJl6ZvSSf6xuhG18RuUHA/KNzH2CfHMvwPlu1rKgyUswnaxIBn4/PszyeJ9jzFmcW8GvUqJVklRociHGTjvzYy4oERli+hZyaJ1hg1NTaYUqVrYd01UFvZpMvj7opj4j8mNi+G4P5Qmo5SivUvrJenWmfXbjgMm9+kSyXRnufXp36l8Fghx7da3w4vXE9eSh6HfzlMzB0xVl/btmdbijOLyd6XbYfIRH0GDLBuakWdSlJNI0h9rF9vOHNXJhv+taF5T7MAUbfA0DcBqCisIHGhLF7h1AoSISDqzLc1VTVobVnrpyTas2TuyiR5WTJx98RxIv6E0eEIB9j08iZi74qlVaD1g1p8Wvsw+rHR/PLkL3aITNRn586dzS+kYA8ENy1h7/l8D+WF5c2PAWDvc1BbQ98ZfUlakmTxG7dwsJpyWDXlnBaQyqJKix/IJNGaaa1Z8eAKxj89nm6XdOPENkm07q4ku4TELxKb1T827N5hZO7I5OiGozaMTDSkc+emL5l4RsggiP4/qy+rra4lcWEi0TfYYOst8jkAACAASURBVECWUnDoXSg6QOeYznj6eHIqXQbXOY3MlRD/R6jMh9xtENQPvH/borEoo8iiDUhAEu0ZycuTKcooInZuLOFDwzm+7bh8unRzOz7YQd9r+hLQKaDJZXj5enHxMxez8tGV8vfiSrwCoN0Iqy87kXCC9v3a03FgR9vEET4dji5GKcU9e+4huEuwbcoVzXf4E9MAqMRnIWutae9is5O7T/L5ZZ+Dhf/LS6IFaipr+Pnhn5ny7yl4eHkQGB6I8lAydcPNJX+bTL/r+jW7nIGzBlJRVMGBbw7YICrRmIyMjOYX8st4KNht9WURwyO4+fubm1//aZGzIG0hAOWF5ax4ZIXtyhbN06odjP4ChrwEnadC99vPHNr16S4Gzh6I8rCsn14SLRD/TjwhUSH0vKwnAEqpM0+1wj2V5pSSlZhF5PjIZpfl4enBxGcnsvbva+Wp1gEGDx7cvAJqKqD4MARZt6/tviX7WPuPtRa/uVokdChcsgYA32Bf9ny2h9xk61ceEnYQ+zIE9jA18YfGmaZjYeo+2PP5HgbNHmRxUS0+0ZbllbH22bVMeWnKOa93jussA6Lc2KEfDxE5IRIvXy+blNfril5UFldydN1vfbW1NbWse24d1RXVNqlDmOzZs6d5BZzaD62jwNPyAXBaa9Y+s5ZOgzs1r+7zKQXVxZD2JcpD0efqPiQtTbJtHcJ6Rz6FpH/XeUjXaqa9OY12fdpZXFyLT7Rr/r6GvjP6XrAwePjQcBkQ5cYOfnewabuu1EN5KIb/aTibXt505rX4t+P59clfOfCtNCnbkqendfOdL+DhA73uteqS5OXJKA9Fz2k9m1d3XXQNJPwBaqvoO6MvqatTbV+HsM7x76BVaJ2Hcvbn0PvK3lYV16ITbW5yLrs/3c2Ev0244FhYXBgZCRnSFOiGaqtrOfTTIZu/aQ76/SCObThG3qE8ijKKWPO3NYx5cgw7P7TBdBRxRq9ezfyAFNzX6kRbml3KhGcnNH/ubF0Ce0DrbpC5kqgJUcz8bqbt6xCW0xpO/godL8wLZXllfDjuQ8ryy6wqstFEq5TqopRapZTap5Taq5S636oanJSu1Sybu4yxfx5L6w4XLsPWukNrWgW1Iu+QbMzsbo5tPEabqDYEhgU2frIVfFr7EDM3hs2vbmbFgysYcscQxv15HOmb0zl1XAbW2UpiYjMXdtg4C7Is25qusqSSlBUpDLltCL0ut10LyAWiZkHaIpSH4ui6o+z/er/96hINKz8JIQOh9UUXHIp/N54+v+tD6/bWLd1pyRNtNfCQ1rofMAK4VynV/KGaBot/N56aihqG/7H+OZRhcWHST+uGkpcn0/NyOzQBYppXu+ujXRzbdIzxT43H29+bftf1Y9cnu+xSX0sUERHRvAKy1lq0PV5tTS1Lb15K4hcOWLGp260w9G3AlNzP7oIQDubXCSatvODlmsoatr2xjREPWD8trNFEq7XO0FpvN/+7CEgCrNtPzMkUpBWw6qlVTP9geoP7j4YNDZN+Wjdk6/7ZswWGBTLq0VFMf2863v6mzaGH3DqEnR/slG4IG6msbNr6xKaLC6Ay75yl9Oqz5m9rKC8o54p3r2h6fZby8jdtv7b3ebpd0o2sPVkUZxbbv15xod1/gYK9F7zs4eXBtYuupdMg6wfEWdVHq5SKBIYAW+o4NlcpFa+Uis/Odt61X7XWLLtzGSMfGkn7vu0bPDdsaBjpm2y096VwuE3/2cSvT/1KVVkVYPrd7/hwB6W5pc3b3qwR458yrS52WvjwcDy8PTi2QfYctYWsrKymX1xdAp0uAdXwW19NVQ2ZOzO5duG1ePo0c/CVpXw7wIH/4FWWRI/LenB4ZRM3lRdNV10Cya9fMBBKa82+xfuIGNG01hSLE61SKgBYAvxJa31Bh5PWer7WOk5rHde+fcMJzEhpa9I4dewUox8Z3ei5F429iLyUPOmndUG11bVseGEDGfEZvDPwHfYs2MMnkz5h2xvbuPn7m207F7IRSikG3zqYHR/scFid7iw2NrZpF+ZuA99OMHZpg6eV5ZVRWVzJTd/e1KxVw6zmHQT9noCdTzL9/ekMnDXQcXW3VOU5cORzqDoFmb/CkvamD2J+5z61pq1NY9XTq5r8vmFRolVKeWNKsp9rrRv+K3VyCfMTiLsnDg+vxm/d08eTgbMGyhukC0pdk0pQRBA3/3AzU/8zlc2vbKbXlb24Y8sddI6xwVq5Vup/fX8OfndQmo9tICEhwfqLji+HNVdAaVqDT7Naa76e8zVb39jajAiboec9UFOKt2cxG/+9kbI860a3CgtVFcGGm2BZdzj2FVTkQfsxcG0BjPnynFO11qx7dh3D7x/e5ETb6Gx9ZRrP/j6QpLV+uUm1OInSnFIOfn+QaW9Os/iaIbcP4dPJnzLhmQkWJWfhHBIXJhJ9o2nh915X9LJbn6ylQi4KwcvPi9wDuVZNdBcX8vPzs+6C6lLYOBsm/AgB3Ro8devrWynOLGbMY2OaEWEzeLaCSabdoI5tOIZ/O38G39LMlbDEhUqPmZZYvDoTvBr+e0pbk0ZpTmmTttI8zZLMMRqYDUxUSu00f1meqZzIzo930ueqPvi1sfx/1A79OxByUQgHfzhox8iELdVU1rB/6X76X9/f6FDOETk+ktQ1qUaH4fIiIyOtuyB/B7SNNW3c3YCyvDI2vLCBGV/McFy/bF20hhWj6HtlGElLZJUom8rZAgkPmpZTjHu90SQLEHlxJHPWzsHTu+l/E5aMOl6vtVZa64Fa68Hmr++bXKNBtNZsn7+dmLnWfyoZcvsQdrwvzceuIuXnFNr1bUdwV+faCaXruK6krUkzOgyXl5RkZfJpPxom/NTgKbpW49fWj3uT7qVt97bNiM4GlILAXvSK3sbRdUfPDOYTzVSRB+uvP2cXnoZorVly0xKObTzWpP2qz9Zi2kLT1qTh4eVBl1FdrL62/w39SVuTJsPtXcTeRXvPNBs7k8jxkaStSZN+2may6olWa9j1f+ds2F2Xb+/8lj0L9tAqqHlvqDYT9Xt8cz7l/tT78fbzNjoa16c1bJ4DXa6BLldbdMmeBXvISsyic2zzx3S0mESbMD+B2Ltim7SEWqvAVvS5po8MinIBVWVVJC9Lpt+1zremSpvubQDIT8k3OBLXVlRUVP/B3HhIW/Tb94X7TAvEe/rWe8meBXs4tv4Yvadbt36tXXW8GIL6UFWYz86PZAnP5tMQNg0Gv2DR2cWZxax4cAVXfXgVXq2av/FIi0i0pwdBNWe4/Ig/jWDLa1uoKpVmHGd28LuDdI7p7NhpGRZSSnHR+ItIWyvNx82Rm9vANnK7n4Ytt0NZpun7E99D+OWm5tg65B/J58f7f2TGwhn4BPjYIdomUh4w+gs8/YL48f4fpfm4OfJ3wclV0PNu8LTsd6w8FFP/M9Vm8+1bRKLd+fFOek/vjV9bK0crnqXjgI50Hd2V+HfjbRiZsLVdn5g2ZHZWF42/SPppm6neebTVZVCRA2OWgI+5nzXzZ9OTTD0CwwK5fsn1dB7i+ClfjarIwz9+DJ1jO5GyIsXoaFxTTQVsmg2llu8tnromleryagbMHGCzMNw+0Z4eBBV7VxMnuZ9l3FPj2PjiRvl06aRKskpIW5tG3xnWbejtSDLyuPnqnUfr5QeXboWwqaYnmFMHYNw30HnqBadqrVn19CryDuZx0bgLF493Cq3agkcr+k4oZ///ZJOBJtnzN9OUrqjZFp1efLKYJTcuoehEA90TTeD2iTZtbdMHQZ2v0+BOhMWFyQhkJ7Xniz30vrJ3s0cI2lNo71Cqy6opSCswOhSXFRBQR7dAbRWsuhSqzAMWC/fAyvGQuxU8zh1MpLXmpwd/4tCPhwjqEuSAiJthyEsM7vYPpr3Y/AeFFqemEvK2wdB36+06OOf0qhoW37CYmLkxTV5qsT5un2hPT+mx1T6S454ax4YXNlBdUW2T8oTt7P5kN4NuGWR0GA1SSnHROGk+bo6wsDr6zdK/NS1M4W1Owr3uM213ln3hdnhrnllD2po0Zv00C9/g+gdJOYUOY/AZ+Q8ydpwkc1em0dG4jvIsqC2HiT+DX0eLLslKzCKgYwAX/+Vim4fj1om2NKeU5O+SGTTbdm++4UPDade3HXsXXbi7gzBOVmIWxSeLiZwQaXQojYqcGMmRX44YHYbLSk5OvvDFQ++YBruc5ukLV5+Afo+feenU8VPUVNbQ+8rezF4x26qFawzV7fcc357Htn8tMToS11BVDKunQernFl+Sm5xLp8GduHbRtXZZB91tE62u1az9x9pmD4Kqy9B5Q0l4twnrrQq72fnxTgbOHtjgtofOovuU7qSsSJH5tE3UvXv3c1+oqQTlCV1mnPu6X2fw8KK2upYtr2/hnUHvkL45nc4xnfFv5++4gG2g77T27F9+lNpTR40OxbnVVpsWpWgzGHrc3fj5mAY/fTD6AwpS7ded4/zvSk1QeLSQTyZ9QkZ8BpP+Ocnm5fe6ohcFaQWc3HPS5mUL61VXVLPn8z0M+r1zNxuf1rZ7W3wCfDi5W/5+muKc6T1aA7WmdYw9L+ybryiq4O0Bb3Pg6wPcuu5W5x341Ig2A4fQ9iIfDn/8htGhOLfs9ab+2KFvW9Qve3zrcb667iuuXXQtbaLa2C0st0u0J3efZH7cfLpP7c4tq28hKML2gx08vDyIuSNGnmqdxE8P/kSXUV0a3V/YmXSf2p2Un2TKRlMUFJz15JH+Day9cKWf/MP5JC5MpFVgK67+7Gpmr5ztUn8fdblu6Wy6hS0yTVkRv6mthu0PwcF3oMN400hzD8tW09r+/namvz+dqIlRdg3R7RLt2mfXMubxMYx5fIxdmxFj7ogh8YtEKksaXtpN2FfiwkRSfkph+vvTjQ7FKpJom+7MPNqaCtjxEPR96MwxXavZ+uZW3hv+HiVZJQCExYbZbDCkkYJ69CKxZAkluTIQ8xxZqyHjJ9PyikqBR+MrOR384SA5B3K48t0r6X2l/VcEc6tEm3coj9RVqcTOtf9Q+KCIILqO6UriwkS71yXqlp2UzQ9/+IHrF1/v/KNHzxM1IYrjW4/LB7UmODOP9sArEBwNnS6hstj0c1zzzBr2fL6HW9fdyvA/Nrxbjys6/Msxdv39CdN+qsIkfxd0nAi+HRo9VWvNhhc38O3t31JR6LiWAbdKtJte3kTsXbEOW0ot9u5Ytr62lVPHTzmkPmGa63bwh4Msu2sZH437iEnPTaLT4E5Gh2U1nwAfwuLCSF2danQoLickJMT0j44TyWvzDMvvXs4rka9QXlDO6EdHc9uG29x2z98hd8SwY3kA+uA7RofiPIL7Q9drLTp12dxl7F24lzu23EH4sHA7B/Ybt0m0JVklJC5MdOin2O5TuhM1KYq3B7zN0llLZXCUA/zwxx/49c+/EtozlNs23taszZiNJs3HTRMaGgrAvlWteW/iT/i39+fepHvxDfHF29/bLZqJ69N1TFdqPUPJ/P4jKJP3G8C08leHcQ2ecviXw+hazYg/jeC2jbcR3MWxW2i6TaLd8voW+t/Qn9YdWjusTg9PD6a+PJU/pvyRjgM78smkT9jw4gZ0rUzbsIdTx0+xd9FeZv88m1EPjyK0Z6jRITWLJFrr5RzIIWm3aT/a7lO6c//h+5n494m0bu+4/++NpJTi9i330HnqjabVr1q6mgr4OsI0IKoOtdW1/PTgTyyfu5yijCI69O9gk914rOX4GhtReLSQVsGtzvS5aa1JeDeBdf80rfDi6e2Jb4gvwV2DCeoaRGVxJQVHCshIyGBuwlxDYvZr48foR0fT//r+LJ21lJSfUpixYIZDk35LsPGljQy+dTD+oa41B7I+nQZ1orygnPzD+bTpZr+pBe6ioqiChdMXMuhJ0zQup9k71sH8Q/3Z+NEkBs0eSOuyDNN84ZaqcB/4tKlzAFR1eTULf7eQ2upa7tx2p83XU7CGUyTawmOF7P1yL3sX7SX/sGmvzri74xg8ZzArH19Jfko+N359I/7t/KmprKEsv4zCtEIKjxXSKrAVA24aQLs+7ewylccaIZEhzFk9hxWPrGDxDYuZvXK2Syyg4IyKTxaz7h/rGPHACNpEtaEku4RdH+9iXuI8o0OzGeWhiL4pmvh345n8wmSjw3FqWmuWz13ORRdfhF9/F1nRyY5yk3PZ+uK3TBj9BFyeCN6BRodkjIJdEHLh/HmtNZ6tPBn0+0H0v74/Hl7Gvg8bVrvWmoT5CXww5gPeHfIuOUk5TPzHRB7OfJg7t91JSXYJb/V/i8CwQG7fdDudYzoT3DWYtj3aEj40nH7X9mPkAyOJuSOGbpd0MzzJnubh5cGUl6agazUbX9xodDguKX1zOv+N+y9FJ4r4YNQHpK1LY8urW+h/fX8Cw9zrDWXEn0aw4/0dVBTJ3MiGxL8dT3ZSNpe+cinFxcVGh2O4UQ+PIv6j41QGT4XvB8CuPxsdkjGUJ3Q+90NqZXEln039jPyUfAbMHGB4kgVQ9lgGLi4uTsfHN7xva8qKFL6b9x2XvXYZ3S7phqeP5wXn1NbUuuwTYeHRQubHzWfmdzMJH+q40W2urCy/jIR3E9j08iamvz+d3lf2JmVFCktnLaWmsoa7tt/llk2si29YTMTICEb8aYTRoTiltLVpfDnjS27beBuhPUMpKioiMNC9PnA1xeIbFjPg5mh6j62EklQIuxyOLYau11u0KpI7qi6vZsEVCwiJCuHK+Vc6dGCcUipBax1X1zHDstiGFzYw/unx9JzWs84kC7hskgUI7hrMtDensXTmUpkr2YiC1AK+ufUbXuv2Gll7srhtw21nJpF3n9KdW9feytSXp7plkgUY+fBINr+ymdrqWqNDcToHlh3gy2u/ZMbCGWcGv9W7H20Lc82Ca+g9vQ+0GQgR06GmDPb9CxKfMTo0x9DatCpYdemZl1Y8vALfEF+ueOcKpxp9bvdMpms1W17fwq5Pd5157fi24+QezCX6pmh7V2+o/tf1JywujA0vbDA6FKeVl5LHR+M/IqhLEPcl38c1n19zwWjidn3aMeS2IQZFaH/hQ8MJ7hrMviX7jA7FaWit2fHBDpbduYyZ382k26RuZ46dnt7T0nl4ehD/Tjy7P99tesE7AC7+Ho58CgffNjY4RyhNh5xN4PXb4MhRj4zidx/9zuke0uwazan0U3w65VMSFySy8rGVJH9n2t5qwwsbGPngSDy9636SdSeTX5zMtje3kX8k3+hQnE5eSh6fTPyEMU+OYcIzE1rMFI26jHxopOxzDBRnFrP++fW82fdNNr60kTmr51zQ9SLNxr/pHNOZFQ+uoDjT3G/t1xEmroCSNPOGC27srIFQ+Yfz+ea2bwjuGuywBYusYZdEW5pTyje3f8O7Me9y0fiLuHXdrdyw9Aa+mfMNiYsSSVuT5tILDVgjKCKIEQ+M4OeHfzY6FKeStjaNjyd8zJgnxxB3V53dGi1K7yt707Z7WxZOX9iiuxq+uPILsvdlc9WHVzFv77w6V3hKTU11fGBOKnxYODF3xrDszmW/bbsY0A0GPw8Fu89pVnU7xanQdgg1VTUsmbmEjgM7OlVz8dnskmgriyoJiw3jtvW3Mf6p8Xh4eRAxIoLL376cJTctIe6eOKf81GEvox4eRcaODI78Kpt9l+WV8e0d37L05qVc9vplkmTNlIdixhczCAwL5LOpn1FeWG50SA5XeKyQ/CP5XPXBVXQZ2aXeN82+ffs6ODLnNv7p8YQPD6e26rw+/qSXYOcTxgTlCL3vg0HPseLhFfi382f4/c67trVdEm1IVAhD5w0ltNe5fSn9ru3HrB9nMerhUfao1ml5+Xox5d9TWH73ckqyS4wOxzDpm9N5e+DbePl5MW/vPPpc1cfokJyKh5cH09+fTqchnfj0kk8pL2hZyTZ5WTI9p/VsdDqGPNGey9PHk3H/N47spGwyd2X+diD2VTi2BE78ZFxw9nLqoGlKU3UxHQd05OpPr3bap1kwYNRx9yndW+SKLn1+14f+1/fnk0mfUJrjxs059djx4Q6+mP4FV7xzBdNen9Yi/wYsoTwUl712GV1Gd2lxT7YHvj1A7+mNb1lWVlbmgGhcT97BPD695FMSF5l3FGvVFkZ+DLufAl0L1W7wIb84FTbNgZ9HknnIn6Rl6cTcEYNfG+dexMS5hma5MaUUE/4+gZ6X9+TTyZ9SlufebxZleWWs/utqvp7zNR+O/ZD1z61nzpo59Lqil9GhOT2lFFP/M5WwYWF8funnlOW7998KQMWpCo5tOEb3qd0bPffMfrTiHP2u7cfsn2fzy+O/sPWNraYXO02CqVtAecC662DleEh5HyoLjQ22qQr3Qeuu5PTaxsKHWlNTWWN0RBaRROtASikm/XMS3aZ044PRH5CzP8fokOxmzd/XcGLbCbqO7cq4p8cxN2Eu7fu2Nzosl6HUb0+2b/R6g9V/Xe3WCTdlRQpdRnehVWDjLR0yj7Z+nQZ34s5tdxI+PJzq8mq+nvM1+5YmkX84Hz3mf9D7ATjxPay72rVGJedthwOvQfg0jpffxUeXLGHcU+OIvtE1pog6xVrHLYlSiskvTKZd73Z8OO5Drpx/JX1+5159lSVZv61L7G5LJjqSUoopL00h9q5Y1j+3ntd7vE7v6b3pf0N/oiZFudX0OEubjQE6dGh8g++WzL+dP/7t/KkqqyJ8WDg7P9zJT3/6iX7X9WPqy7/j+xd8UB6KuHYptA/eBpE3GR1y/WprIH0pxN9HbcxbVOSVEXxRMDO+mEHUxCijo7OYYUswCtPCHV/O+JK4u+MY88QYp+7Mt8bKx1dSUVTB5W9ebnQobqXoRBF7v9rL3oV7KckuYc6aOQSFO8ca381RW13LS51e4q4dd1m0T+ihQ4fo0aOHAyJzL6eXtD34/UGObzvOtje2MHzqJsY9OggG/MXUvOxs9r2APvo/DuY8zC8vZNPz8p5c8vwlRkdVp2YvwaiUulQpdUApdUgp9bhtw2u5woeGc8fmO9j31T6W373cLZbgK8srY/t/tzP60dFGh+J2AsMCGXH/CG7fdDsxd8bw2ZTP3KKv/+j6owR3DbZ4M+709HQ7R+SeTq+W1HNaTy7+y8X84eD99HngOWqO/szau/5AWZ4TDdI88BqcXA29H+DrBY/x8z9OMuHZCUx6bpLRkTVJo4lWKeUJvAlcBvQDblJK9bN3YC1FYFggc9bOoTCtkIVXLXT5N84tr22h9+96E3JRiNGhuLXRj46mx7QefD7tcyqLXXOBi9yDuXw37zsWXb2IYfcNs/i66GjX6Jdzdr4hvnSI7UvVqB8oLBvIG73fJP6NtdijldMqJ35AJ77A/jXeaA9vRj40krt33U2fq/q4bKufJU+0w4BDWuvDWutKYCFwlX3DallaBbbipmU30bZnW94e+DaHfjpkdEhWKUgtIP7deJbOWsqWV7cw9omxRofk9pRSTP7XZNr3b8/HEz4mNznX6JAssm/xPr6941ve6v8WH4z6AL+2ftybdK9Va1knJyfbMcKWx7dtMFd+ehdzfphEwstfcXK9gdt7njpI7tL7+fjlR1n/chLlBeV0GtSp3o1nXIUliTYcOHbW9+nm14QNeXp7cukrl/K7j37HsjuX8f1931NVWmV0WA0qSC3gm9u+YX7cfNI3phM5IZK52+fStkdbo0NrEZRSTH9vOoNuGcT7o94nYX4CutZ5R5JuenkTvzz5C52GdOLqz67moYyHmPjsRAI6BVhVTk2Na0zpcDXt42KYu3IEnbKvJeGVbziRcMKxAdTWkLW/mPef/j19rhvObRtuc/r5sZZqdDCUUupa4FKt9R3m72cDw7XW95133lxgLkDXrl1j09LS7BNxC1BeUM73933PiW0nuPrTqwkf5lyfa7TWrH9+PZte2kTcvDhGPjjSbf6HcFXZ+7L55rZvyNmfQ/jQcLqO7crIh0bi09o5ljrd8cEO1jyzhlvX3WpxX2x9CgoKCAmRrgm7OfoVu979kZ/f70ufKa2Z+Ndh+EfF2G2PW11dTvKbz6BLc+j9+LsUHS8iKML1Bvk1NBjKkkQ7Evir1nqq+fsnALTWz9V3jYw6to29X+3lh/t+oNf0Xgy5bQgRIyIM76Oora7lu3u/48S2E8xcPlOm7ziZkuwSjm89zp7P9nByz0muX3x9nQvz21NVWRWJCxM5vvW4aU1zDXs+38Mtq2+hXe/mx7J69Wouvvji5gcq6qdrKSuoYNVdf8Gr6ghTbt/LSe6lw7R5KA/L3oO01pRml+LbxpeKUxWkrU2j4lQF+Sn5RE2MIvLiSN4Z8Aolx0/iGwjT3r2OqEtdd7OZ5iZaLyAZmAQcB7YBM7XWe+u7RhKt7ZRklbDjgx3s/HAnKJj8r8kWzze0leLMYgrSCig6UcSO93dQW1XLdYuvs2hxAWEMrTXb39vOr0/+yoS/T2DAzAF2X/aypqqGVU+vYsd7OwgfFk6Py3pQVVpFRVEF0TdE0yHaNvNfDxw4QO/ejv1/oCXTNbXUZGzgvSkbOJXlQcdeiv6zxxJ3zwh2frSTgtQCcg/kUppTyuyfZ7P22bVseW0LVaVVeLXy4raNt1FbXcuqp1bh09qHkKgQ+kzvQefYCLJXfkSr1rUEDJuDh5dr98M2K9GaC5gGvAJ4Ah9orf/R0PmSaG1Pa83hlYf5ft73dI7tzGWvXUbrDvbbvzVpaRK7P9vN8a3HqS6rpk23NgSGBdJxcEfG/d84t1oswZ1l7sxk1VOrSFubRrfJ3Rh23zAiL460S13f3fsd+YfymfbWNNp2t18/vSRaY2itKTqWQ9aiB/CqTiVyTGcS1k3gVEkUbb1+oONld9JpxACqy6upOFWBZytPfIN9Lywoay3E3weDX4SwqY6/ETtpdqK1liRa+6kqrWL1X1ez/b/biZoYRc8retL7yt74t/O3Sflaa9Y+u5Yd7+9g0j8ntVqZQwAACMNJREFUETEigpCoEMObrEXzlOWVsW/xPtY/t56Ogzoy+V+TL9hdqzm2v7edTf/exB1b7rD7k7M0HTuBjBVQngUdJ4BPW9j7Tzj0DvR7DLrdCq3O+tvStVBrnoK2cRbkboWYf0OXa+3W72sESbRuqCSrhEM/HiJ5eTKHVx5m8K2DGf3IaKtHcJ6turya7+Z9x8ndJ5m5fGazyhLOqbq8mi2vbWHDvzYQ2jOULmO6EDk+kqhJUXj7eTepzGObjrHwqoXcuu5Wm/TBNkYGQzmpwn2w93mI/jPUVsPWO6Esw/QV+x/ocTekvAeRM8HLfq1xRpFE6+ZOHT/Fhhc2sPuz3YREhuDl63Xmy9vPG58AHwLCAkxNvwM70mVUlzNNv5UllaStTWPvor0c+PYA3ad0Z/r7051mtKqwj6rSKo5vPc7R9Uc58ssRMrZn0G1yN7pP7U7nIZ1p3789SikKjxVSdKKImsoaaqtqqa3+7St7XzYpK1LI3pvNNQuuodfljtmZad26dYwdK3O1nVp5DpzaD36dTV9etmlxc2aSaFuI0pxSCo8WUlVWRXV59ZmvilMVFJ0oouh4Ece3HifvUB6R4yMpziwmKzGLTkM60e/afvS/vr+MIm6hSnNKOfDtAVJXp3Jy10lyDuSAhsDwQILCg/Dy88LDy+O3L08PQqJC6D61O11Hd8XL13H7k2zcuJFRo0Y5rD4hLCGJVpyjOLOYI6uOEBgWSPjQcLz9m9ZkKNxXTWUNHl4eFk/lcKScnBzatXPslCUhGtPsTQWEewnoFMCAmwYQOT5Skqyok6ePp1MmWYDExESjQxDCKpJohRAuJSIiwugQhLCKJFohhEuprHTN3YpEyyWJVgjhUrKysowOQQirSKIVQriU2NhYo0MQwiqSaIUQLiUhIcHoEISwiiRaIYRL8fOTLRmFa5FEK4RwKZGRkUaHIIRVJNEKIVxKUlKS0SEIYRW7rAyllCoCDti8YOO1A3KMDsIO3PW+wH3vzV3vC9z33tz1vsB9782a+7pIa92+rgP2WqD0QH1LUbkypVS83Jdrcdd7c9f7Ave9N3e9L3Dfe7PVfUnTsRBCCGFHkmiFEEIIO7JXop1vp3KNJvfletz13tz1vsB9781d7wvc995scl92GQwlhBBCCBNpOhZCCCHsSBKtEEIIYUc2TbRKqUuVUgeUUoeUUo/bsmxHUkp1UUqtUkrtU0rtVUrdb369rVLqZ6XUQfN/2xgda1MppTyVUjuUUsvN30cppbaYf3eLlFI+RsdoLaVUiFJqsVJqv1IqSSk10l1+Z0qpB8x/i4lKqS+UUr6u+DtTSn2glMpSSiWe9VqdvyNl8pr5/nYrpWKMi7xx9dzbi+a/x91Kqf8ppULOOvaE+d4OKKWmGhN14+q6r7OOPaSU0kqpdubvXf53Zn79D+bf216l1L/Oer1JvzObJVqllCfwJnAZ0A+4SSnVz1blO1g18JDWuh8wArjXfC+PA79orXsCv5i/d1X3A2cvsfMC8B+tdQ8gH7jdkKia51XgR611H2AQpvtz+d+ZUioc+CMQp7WOBjyBG3HN39lHwKXnvVbf7+gyoKf5ay7wtoNibKqPuPDefgaitdYDgWTgCQDz+8mNQH/zNW+Z30Od0UdceF8opboAU4CjZ73s8r8zpdQE4CpgkNa6P/CS+fUm/85s+UQ7DDj0/+2dXYhVVRTHfwumGRoFPxItvcFMkb2qIAh9kJMPZuL00EMgZNRTb70o1EDQm0/WWz0oYSWJ2aBDIEQf1JNaDZnRB40peYcxhdCiQI3+Pux16TB4i7n3njnuw/rB4e6ve1mL/z17Hdbe5xxJP0u6Bhx0Y7ND0oykSS//QZqwV5H82e/D9gNPVGNhd5hZA3gc2Ot1A0aAwz4kO9/MbBHwMLAPQNI1SZepiWakh8vcbmZ9wCAwQ4aaSfoc+G1WczuNRoG3lDgOLDazu+bH0rlzM98kfSjpb68eBxpeHgUOSroq6SwwRZpDbznaaAbwKrALKO6ozV4z4Hlgt6SrPqb1AuSONetloF0FnC/Um96WNWY2BKwFTgArJM141wVgRUVmdctrpBPkH6/fAVwuTAg5ajcMXALe9JT4XjNbQA00kzRNuqr+hRRgrwBfkb9mLdppVLc55VngmJez9s3MRoFpSadmdWXtl7MaeMiXZT4zs/Xe3rFvsRnqPzCzhcD7wAuSfi/2Kd0Xld29UWa2FbgoqW4v9ewD1gGvS1oL/MmsNHHGmi0hXU0PAyuBBdwklVcHctXo/zCzMdKS1IGqbekWMxsEXgJertqWkugDlpKWDXcChzzr1zG9DLTTwN2FesPbssTMbiMF2QOSxr3511YaxD8vtvv+LcwDwDYzO0dK74+Q1jYXe1oS8tSuCTQlnfD6YVLgrYNmm4Czki5Jug6Mk3TMXbMW7TSqxZxiZs8AW4Ht+vfBBTn7di/pou+UzyMNYNLM7iRvv1o0gXFPf58kZf6W0YVvvQy0XwD3+U7IftKi8UQPf3/e8KuXfcD3kvYUuiaAHV7eARydb9u6RdKLkhqShkgafSJpO/Ap8KQPy843SReA82Z2vzc9CnxHDTQjpYw3mNmg/zdbvmWtWYF2Gk0AT/tO1g3AlUKKOQvMbDNpmWabpL8KXRPAU2Y2YGbDpM1DJ6uwca5IOi1puaQhn0eawDo/B7PXDDgCbAQws9VAP+kNPp1rJqlnB7CFtLPuDDDWy9+ezwN4kJS++gb42o8tpLXMj4GfgI+ApVXb2qWfjwAfePke/9NMAe8BA1Xb14E/a4AvXbcjwJK6aAa8AvwAfAu8DQzkqBnwLmmd+Tppgn6unUaAke5kOAOcJu26rtyHOfo2RVrXa80jbxTGj7lvPwKPVW3/XPya1X8OWFYjzfqBd/xcmwRGutUsHsEYBEEQBCUSm6GCIAiCoEQi0AZBEARBiUSgDYIgCIISiUAbBEEQBCUSgTYIgiAISiQCbRAEQRCUSATaIAiCICiRG0aREM8nPjA6AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_test = X[splits[2]]\n",
    "y_test = y[splits[2]]\n",
    "plot_forecast(X_test, y_test, y_test_preds, sel_vars=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2af86a77",
   "metadata": {},
   "source": [
    "# Inference 🚀 "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56d4a33f",
   "metadata": {},
   "source": [
    "## Prepare dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3cec6e2e",
   "metadata": {},
   "source": [
    "If you want to use the model with new data, you'll need to first prepare the data following the process we defined before. \n",
    "\n",
    "Let's assume we want to create a prediction for '2020-06-30'. In our case, we need a history of 104 time steps to predict the next 60 days. We'll prepare data in the following way: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d52c7206",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2018-07-10', '2018-07-17', '2018-07-24', '2018-07-31',\n",
       "               '2018-08-07', '2018-08-14', '2018-08-21', '2018-08-28',\n",
       "               '2018-09-04', '2018-09-11',\n",
       "               ...\n",
       "               '2020-04-28', '2020-05-05', '2020-05-12', '2020-05-19',\n",
       "               '2020-05-26', '2020-06-02', '2020-06-09', '2020-06-16',\n",
       "               '2020-06-23', '2020-06-30'],\n",
       "              dtype='datetime64[ns]', length=104, freq='7D')"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fcst_date = \"2020-06-30\"\n",
    "\n",
    "dates = pd.date_range(start=None, end=fcst_date, periods=fcst_history, freq=freq)\n",
    "dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4a27a5fb",
   "metadata": {},
   "outputs": [
    {
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       "\n",
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       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>% WEIGHTED ILI</th>\n",
       "      <th>%UNWEIGHTED ILI</th>\n",
       "      <th>AGE 0-4</th>\n",
       "      <th>AGE 5-24</th>\n",
       "      <th>ILITOTAL</th>\n",
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       "      <th>OT</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-07-10</td>\n",
       "      <td>2.286760</td>\n",
       "      <td>2.266120</td>\n",
       "      <td>6754</td>\n",
       "      <td>10005</td>\n",
       "      <td>27854</td>\n",
       "      <td>2568</td>\n",
       "      <td>1229148</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-07-17</td>\n",
       "      <td>2.012170</td>\n",
       "      <td>1.964840</td>\n",
       "      <td>6325</td>\n",
       "      <td>7904</td>\n",
       "      <td>23147</td>\n",
       "      <td>2542</td>\n",
       "      <td>1178058</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-07-24</td>\n",
       "      <td>1.713230</td>\n",
       "      <td>1.722010</td>\n",
       "      <td>5698</td>\n",
       "      <td>6621</td>\n",
       "      <td>20610</td>\n",
       "      <td>2516</td>\n",
       "      <td>1196855</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-07-31</td>\n",
       "      <td>1.566780</td>\n",
       "      <td>1.572800</td>\n",
       "      <td>5369</td>\n",
       "      <td>6207</td>\n",
       "      <td>18864</td>\n",
       "      <td>2485</td>\n",
       "      <td>1199386</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-08-07</td>\n",
       "      <td>1.498270</td>\n",
       "      <td>1.457180</td>\n",
       "      <td>4789</td>\n",
       "      <td>5906</td>\n",
       "      <td>17107</td>\n",
       "      <td>2444</td>\n",
       "      <td>1173980</td>\n",
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       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>2020-06-02</td>\n",
       "      <td>0.839059</td>\n",
       "      <td>0.846722</td>\n",
       "      <td>2756</td>\n",
       "      <td>3528</td>\n",
       "      <td>12913</td>\n",
       "      <td>3258</td>\n",
       "      <td>1525058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>2020-06-09</td>\n",
       "      <td>0.895958</td>\n",
       "      <td>0.908885</td>\n",
       "      <td>3203</td>\n",
       "      <td>3778</td>\n",
       "      <td>13979</td>\n",
       "      <td>3254</td>\n",
       "      <td>1538038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>2020-06-16</td>\n",
       "      <td>0.910926</td>\n",
       "      <td>0.941625</td>\n",
       "      <td>3478</td>\n",
       "      <td>3796</td>\n",
       "      <td>14389</td>\n",
       "      <td>3177</td>\n",
       "      <td>1528103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>2020-06-23</td>\n",
       "      <td>0.946945</td>\n",
       "      <td>0.972185</td>\n",
       "      <td>3734</td>\n",
       "      <td>3818</td>\n",
       "      <td>14999</td>\n",
       "      <td>3066</td>\n",
       "      <td>1542813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>0.963716</td>\n",
       "      <td>1.013760</td>\n",
       "      <td>3955</td>\n",
       "      <td>3843</td>\n",
       "      <td>15307</td>\n",
       "      <td>3027</td>\n",
       "      <td>1509928</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>104 rows × 8 columns</p>\n",
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       "\n",
       "    .colab-df-convert:hover {\n",
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       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
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       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
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       "  </style>\n",
       "\n",
       "      <script>\n",
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       "          document.querySelector('#df-054f5e72-329f-4f14-8d4a-c70413b67833 button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-054f5e72-329f-4f14-8d4a-c70413b67833');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
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       "  "
      ],
      "text/plain": [
       "          date  % WEIGHTED ILI  %UNWEIGHTED ILI  AGE 0-4  AGE 5-24  ILITOTAL  \\\n",
       "0   2018-07-10        2.286760         2.266120     6754     10005     27854   \n",
       "1   2018-07-17        2.012170         1.964840     6325      7904     23147   \n",
       "2   2018-07-24        1.713230         1.722010     5698      6621     20610   \n",
       "3   2018-07-31        1.566780         1.572800     5369      6207     18864   \n",
       "4   2018-08-07        1.498270         1.457180     4789      5906     17107   \n",
       "..         ...             ...              ...      ...       ...       ...   \n",
       "99  2020-06-02        0.839059         0.846722     2756      3528     12913   \n",
       "100 2020-06-09        0.895958         0.908885     3203      3778     13979   \n",
       "101 2020-06-16        0.910926         0.941625     3478      3796     14389   \n",
       "102 2020-06-23        0.946945         0.972185     3734      3818     14999   \n",
       "103 2020-06-30        0.963716         1.013760     3955      3843     15307   \n",
       "\n",
       "     NUM. OF PROVIDERS       OT  \n",
       "0                 2568  1229148  \n",
       "1                 2542  1178058  \n",
       "2                 2516  1196855  \n",
       "3                 2485  1199386  \n",
       "4                 2444  1173980  \n",
       "..                 ...      ...  \n",
       "99                3258  1525058  \n",
       "100               3254  1538038  \n",
       "101               3177  1528103  \n",
       "102               3066  1542813  \n",
       "103               3027  1509928  \n",
       "\n",
       "[104 rows x 8 columns]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df = get_long_term_forecasting_data(dsid, return_df=True)\n",
    "new_df = new_df[new_df[datetime_col].isin(dates)].reset_index(drop=True)\n",
    "new_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ea997f0",
   "metadata": {},
   "source": [
    "## Preprocess dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fc74b9a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initial memory usage: 6.62 KB   \n",
      "Final memory usage  : 3.38 KB    (-49.1%)\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>% WEIGHTED ILI</th>\n",
       "      <th>%UNWEIGHTED ILI</th>\n",
       "      <th>AGE 0-4</th>\n",
       "      <th>AGE 5-24</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-07-10</td>\n",
       "      <td>0.466273</td>\n",
       "      <td>0.520330</td>\n",
       "      <td>2.155252</td>\n",
       "      <td>1.522339</td>\n",
       "      <td>2.307957</td>\n",
       "      <td>2.841660</td>\n",
       "      <td>3.720170</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-07-17</td>\n",
       "      <td>0.245602</td>\n",
       "      <td>0.258392</td>\n",
       "      <td>1.945097</td>\n",
       "      <td>1.042539</td>\n",
       "      <td>1.757253</td>\n",
       "      <td>2.787683</td>\n",
       "      <td>3.483340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-07-24</td>\n",
       "      <td>0.005362</td>\n",
       "      <td>0.047270</td>\n",
       "      <td>1.637947</td>\n",
       "      <td>0.749544</td>\n",
       "      <td>1.460432</td>\n",
       "      <td>2.733706</td>\n",
       "      <td>3.570474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-07-31</td>\n",
       "      <td>-0.112330</td>\n",
       "      <td>-0.082456</td>\n",
       "      <td>1.476780</td>\n",
       "      <td>0.655000</td>\n",
       "      <td>1.256155</td>\n",
       "      <td>2.669350</td>\n",
       "      <td>3.582207</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-08-07</td>\n",
       "      <td>-0.167387</td>\n",
       "      <td>-0.182978</td>\n",
       "      <td>1.192654</td>\n",
       "      <td>0.586261</td>\n",
       "      <td>1.050592</td>\n",
       "      <td>2.584233</td>\n",
       "      <td>3.464436</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>2020-06-02</td>\n",
       "      <td>-0.697154</td>\n",
       "      <td>-0.713722</td>\n",
       "      <td>0.196745</td>\n",
       "      <td>0.043204</td>\n",
       "      <td>0.559907</td>\n",
       "      <td>4.274116</td>\n",
       "      <td>5.091879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>2020-06-09</td>\n",
       "      <td>-0.651428</td>\n",
       "      <td>-0.659677</td>\n",
       "      <td>0.415718</td>\n",
       "      <td>0.100296</td>\n",
       "      <td>0.684625</td>\n",
       "      <td>4.265812</td>\n",
       "      <td>5.152048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>2020-06-16</td>\n",
       "      <td>-0.639399</td>\n",
       "      <td>-0.631212</td>\n",
       "      <td>0.550432</td>\n",
       "      <td>0.104406</td>\n",
       "      <td>0.732594</td>\n",
       "      <td>4.105958</td>\n",
       "      <td>5.105994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>2020-06-23</td>\n",
       "      <td>-0.610453</td>\n",
       "      <td>-0.604642</td>\n",
       "      <td>0.675839</td>\n",
       "      <td>0.109430</td>\n",
       "      <td>0.803962</td>\n",
       "      <td>3.875520</td>\n",
       "      <td>5.174183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>-0.596975</td>\n",
       "      <td>-0.568496</td>\n",
       "      <td>0.784101</td>\n",
       "      <td>0.115139</td>\n",
       "      <td>0.839997</td>\n",
       "      <td>3.794555</td>\n",
       "      <td>5.021743</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>104 rows × 8 columns</p>\n",
       "</div>\n",
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       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-932a06df-4ac8-4d47-a7e9-b9b4b2bc7b9a button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-932a06df-4ac8-4d47-a7e9-b9b4b2bc7b9a');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "          date  % WEIGHTED ILI  %UNWEIGHTED ILI   AGE 0-4  AGE 5-24  ILITOTAL  \\\n",
       "0   2018-07-10        0.466273         0.520330  2.155252  1.522339  2.307957   \n",
       "1   2018-07-17        0.245602         0.258392  1.945097  1.042539  1.757253   \n",
       "2   2018-07-24        0.005362         0.047270  1.637947  0.749544  1.460432   \n",
       "3   2018-07-31       -0.112330        -0.082456  1.476780  0.655000  1.256155   \n",
       "4   2018-08-07       -0.167387        -0.182978  1.192654  0.586261  1.050592   \n",
       "..         ...             ...              ...       ...       ...       ...   \n",
       "99  2020-06-02       -0.697154        -0.713722  0.196745  0.043204  0.559907   \n",
       "100 2020-06-09       -0.651428        -0.659677  0.415718  0.100296  0.684625   \n",
       "101 2020-06-16       -0.639399        -0.631212  0.550432  0.104406  0.732594   \n",
       "102 2020-06-23       -0.610453        -0.604642  0.675839  0.109430  0.803962   \n",
       "103 2020-06-30       -0.596975        -0.568496  0.784101  0.115139  0.839997   \n",
       "\n",
       "     NUM. OF PROVIDERS        OT  \n",
       "0             2.841660  3.720170  \n",
       "1             2.787683  3.483340  \n",
       "2             2.733706  3.570474  \n",
       "3             2.669350  3.582207  \n",
       "4             2.584233  3.464436  \n",
       "..                 ...       ...  \n",
       "99            4.274116  5.091879  \n",
       "100           4.265812  5.152048  \n",
       "101           4.105958  5.105994  \n",
       "102           3.875520  5.174183  \n",
       "103           3.794555  5.021743  \n",
       "\n",
       "[104 rows x 8 columns]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from tsai.inference import load_learner\n",
    "\n",
    "learn = load_learner('models/patchTST.pt')\n",
    "new_df = learn.transform(new_df)\n",
    "new_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f288fe9",
   "metadata": {},
   "source": [
    "## Apply sliding window"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "84fc1984",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 7, 104)"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_feat = new_df.columns[1:]\n",
    "new_X, _ = prepare_forecasting_data(new_df, fcst_history=fcst_history, fcst_horizon=0, x_vars=x_vars, y_vars=None)\n",
    "new_X.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1067bd58",
   "metadata": {},
   "source": [
    "## Cast predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4078b1fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
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       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
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       "    }\n",
       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
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       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
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      "text/plain": [
       "<IPython.core.display.HTML object>"
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       "<IPython.core.display.HTML object>"
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     "metadata": {},
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    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-84ae4ea5-3dc3-44ee-b568-45515f2bc3c2\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>% WEIGHTED ILI</th>\n",
       "      <th>%UNWEIGHTED ILI</th>\n",
       "      <th>AGE 0-4</th>\n",
       "      <th>AGE 5-24</th>\n",
       "      <th>ILITOTAL</th>\n",
       "      <th>NUM. OF PROVIDERS</th>\n",
       "      <th>OT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-07-07</td>\n",
       "      <td>1.411405</td>\n",
       "      <td>1.591912</td>\n",
       "      <td>2702.484841</td>\n",
       "      <td>3332.729258</td>\n",
       "      <td>17439.662198</td>\n",
       "      <td>3080.320316</td>\n",
       "      <td>1.342750e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-07-14</td>\n",
       "      <td>1.158573</td>\n",
       "      <td>1.290422</td>\n",
       "      <td>2218.531072</td>\n",
       "      <td>2579.458693</td>\n",
       "      <td>14030.595373</td>\n",
       "      <td>3066.143876</td>\n",
       "      <td>1.326612e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-07-21</td>\n",
       "      <td>0.976981</td>\n",
       "      <td>1.071673</td>\n",
       "      <td>2071.280693</td>\n",
       "      <td>2982.152038</td>\n",
       "      <td>12263.867698</td>\n",
       "      <td>3060.660311</td>\n",
       "      <td>1.342087e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-07-28</td>\n",
       "      <td>0.864371</td>\n",
       "      <td>0.942170</td>\n",
       "      <td>2075.758784</td>\n",
       "      <td>3694.024325</td>\n",
       "      <td>11775.541865</td>\n",
       "      <td>3049.166623</td>\n",
       "      <td>1.364134e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-08-04</td>\n",
       "      <td>0.842937</td>\n",
       "      <td>0.925972</td>\n",
       "      <td>2110.089350</td>\n",
       "      <td>4409.159154</td>\n",
       "      <td>12181.050698</td>\n",
       "      <td>3030.611186</td>\n",
       "      <td>1.386885e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2020-08-11</td>\n",
       "      <td>0.830736</td>\n",
       "      <td>0.907552</td>\n",
       "      <td>2196.719302</td>\n",
       "      <td>5443.275954</td>\n",
       "      <td>12223.428181</td>\n",
       "      <td>3005.961789</td>\n",
       "      <td>1.401025e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2020-08-18</td>\n",
       "      <td>0.824093</td>\n",
       "      <td>0.910349</td>\n",
       "      <td>2374.147513</td>\n",
       "      <td>6513.932715</td>\n",
       "      <td>11808.576963</td>\n",
       "      <td>2985.465491</td>\n",
       "      <td>1.395366e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2020-08-25</td>\n",
       "      <td>0.838693</td>\n",
       "      <td>0.988355</td>\n",
       "      <td>2342.311507</td>\n",
       "      <td>6335.618813</td>\n",
       "      <td>11544.068963</td>\n",
       "      <td>2967.074970</td>\n",
       "      <td>1.399139e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2020-09-01</td>\n",
       "      <td>0.857786</td>\n",
       "      <td>1.100826</td>\n",
       "      <td>2174.361215</td>\n",
       "      <td>5172.548823</td>\n",
       "      <td>11660.298057</td>\n",
       "      <td>2940.809491</td>\n",
       "      <td>1.386037e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2020-09-08</td>\n",
       "      <td>0.897425</td>\n",
       "      <td>1.191296</td>\n",
       "      <td>2298.593304</td>\n",
       "      <td>4835.880476</td>\n",
       "      <td>12828.926618</td>\n",
       "      <td>2923.562815</td>\n",
       "      <td>1.386792e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2020-09-15</td>\n",
       "      <td>0.929066</td>\n",
       "      <td>1.277326</td>\n",
       "      <td>2403.423011</td>\n",
       "      <td>4836.815652</td>\n",
       "      <td>13519.800538</td>\n",
       "      <td>2889.795275</td>\n",
       "      <td>1.382223e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2020-09-22</td>\n",
       "      <td>1.005786</td>\n",
       "      <td>1.451736</td>\n",
       "      <td>2366.960229</td>\n",
       "      <td>4946.945534</td>\n",
       "      <td>14913.654047</td>\n",
       "      <td>2876.090613</td>\n",
       "      <td>1.384362e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2020-09-29</td>\n",
       "      <td>1.052060</td>\n",
       "      <td>1.624004</td>\n",
       "      <td>2154.857600</td>\n",
       "      <td>4641.439955</td>\n",
       "      <td>15767.764470</td>\n",
       "      <td>2854.507203</td>\n",
       "      <td>1.374647e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2020-10-06</td>\n",
       "      <td>0.952962</td>\n",
       "      <td>1.521194</td>\n",
       "      <td>2011.039889</td>\n",
       "      <td>4609.024371</td>\n",
       "      <td>15220.053510</td>\n",
       "      <td>2833.764565</td>\n",
       "      <td>1.377211e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2020-10-13</td>\n",
       "      <td>0.775218</td>\n",
       "      <td>1.249543</td>\n",
       "      <td>1913.729149</td>\n",
       "      <td>4564.133866</td>\n",
       "      <td>13353.708030</td>\n",
       "      <td>2814.412226</td>\n",
       "      <td>1.366981e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2020-10-20</td>\n",
       "      <td>0.621133</td>\n",
       "      <td>0.971701</td>\n",
       "      <td>1941.265439</td>\n",
       "      <td>5010.430969</td>\n",
       "      <td>11887.493575</td>\n",
       "      <td>2801.313365</td>\n",
       "      <td>1.373712e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2020-10-27</td>\n",
       "      <td>0.479081</td>\n",
       "      <td>0.731611</td>\n",
       "      <td>2135.351552</td>\n",
       "      <td>5452.592733</td>\n",
       "      <td>10948.119749</td>\n",
       "      <td>2804.629482</td>\n",
       "      <td>1.396905e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2020-11-03</td>\n",
       "      <td>0.377170</td>\n",
       "      <td>0.575832</td>\n",
       "      <td>2262.961799</td>\n",
       "      <td>5483.482491</td>\n",
       "      <td>10580.607087</td>\n",
       "      <td>2806.798882</td>\n",
       "      <td>1.447871e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2020-11-10</td>\n",
       "      <td>0.329128</td>\n",
       "      <td>0.489898</td>\n",
       "      <td>2577.009487</td>\n",
       "      <td>6049.818211</td>\n",
       "      <td>10677.091744</td>\n",
       "      <td>2806.715276</td>\n",
       "      <td>1.501868e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2020-11-17</td>\n",
       "      <td>0.409451</td>\n",
       "      <td>0.549002</td>\n",
       "      <td>3170.148406</td>\n",
       "      <td>7986.377921</td>\n",
       "      <td>12751.867479</td>\n",
       "      <td>2822.747823</td>\n",
       "      <td>1.578970e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2020-11-24</td>\n",
       "      <td>0.576136</td>\n",
       "      <td>0.736760</td>\n",
       "      <td>3871.406976</td>\n",
       "      <td>10577.198365</td>\n",
       "      <td>15642.055438</td>\n",
       "      <td>2834.115298</td>\n",
       "      <td>1.631351e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2020-12-01</td>\n",
       "      <td>0.750226</td>\n",
       "      <td>0.936262</td>\n",
       "      <td>4419.460440</td>\n",
       "      <td>12819.594867</td>\n",
       "      <td>18592.547582</td>\n",
       "      <td>2842.341104</td>\n",
       "      <td>1.677509e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2020-12-08</td>\n",
       "      <td>0.901229</td>\n",
       "      <td>1.095970</td>\n",
       "      <td>4992.888899</td>\n",
       "      <td>13501.629240</td>\n",
       "      <td>20888.320806</td>\n",
       "      <td>2857.210742</td>\n",
       "      <td>1.714638e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2020-12-15</td>\n",
       "      <td>1.093728</td>\n",
       "      <td>1.283268</td>\n",
       "      <td>5540.935185</td>\n",
       "      <td>13499.067230</td>\n",
       "      <td>23528.232899</td>\n",
       "      <td>2890.156000</td>\n",
       "      <td>1.772600e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2020-12-22</td>\n",
       "      <td>1.294767</td>\n",
       "      <td>1.484959</td>\n",
       "      <td>6309.568185</td>\n",
       "      <td>14148.514800</td>\n",
       "      <td>26269.423621</td>\n",
       "      <td>2991.217331</td>\n",
       "      <td>1.869280e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2020-12-29</td>\n",
       "      <td>1.473360</td>\n",
       "      <td>1.727058</td>\n",
       "      <td>7112.021938</td>\n",
       "      <td>15853.113699</td>\n",
       "      <td>28846.811324</td>\n",
       "      <td>3171.841339</td>\n",
       "      <td>1.973652e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2021-01-05</td>\n",
       "      <td>1.735037</td>\n",
       "      <td>2.132600</td>\n",
       "      <td>8317.052175</td>\n",
       "      <td>18584.748635</td>\n",
       "      <td>32443.341275</td>\n",
       "      <td>3345.058722</td>\n",
       "      <td>2.056525e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2021-01-12</td>\n",
       "      <td>1.964189</td>\n",
       "      <td>2.525607</td>\n",
       "      <td>9188.190908</td>\n",
       "      <td>20427.971685</td>\n",
       "      <td>35072.670969</td>\n",
       "      <td>3462.316597</td>\n",
       "      <td>2.074185e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2021-01-19</td>\n",
       "      <td>2.119312</td>\n",
       "      <td>2.739661</td>\n",
       "      <td>10084.885565</td>\n",
       "      <td>21021.533196</td>\n",
       "      <td>36649.356658</td>\n",
       "      <td>3511.999428</td>\n",
       "      <td>2.059325e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2021-01-26</td>\n",
       "      <td>2.280924</td>\n",
       "      <td>2.860568</td>\n",
       "      <td>11464.390012</td>\n",
       "      <td>21103.179251</td>\n",
       "      <td>39314.352275</td>\n",
       "      <td>3535.432169</td>\n",
       "      <td>2.032153e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2021-02-02</td>\n",
       "      <td>2.512907</td>\n",
       "      <td>3.056032</td>\n",
       "      <td>13033.252807</td>\n",
       "      <td>21090.914177</td>\n",
       "      <td>43351.407937</td>\n",
       "      <td>3550.155152</td>\n",
       "      <td>1.997569e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2021-02-09</td>\n",
       "      <td>2.860601</td>\n",
       "      <td>3.520307</td>\n",
       "      <td>14393.599437</td>\n",
       "      <td>20978.455100</td>\n",
       "      <td>48585.487142</td>\n",
       "      <td>3559.303154</td>\n",
       "      <td>1.948657e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2021-02-16</td>\n",
       "      <td>3.350611</td>\n",
       "      <td>4.291435</td>\n",
       "      <td>15698.116188</td>\n",
       "      <td>21661.968840</td>\n",
       "      <td>55116.724284</td>\n",
       "      <td>3560.076972</td>\n",
       "      <td>1.888864e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2021-02-23</td>\n",
       "      <td>3.795318</td>\n",
       "      <td>5.040864</td>\n",
       "      <td>16393.904659</td>\n",
       "      <td>22417.901640</td>\n",
       "      <td>60966.216939</td>\n",
       "      <td>3546.928039</td>\n",
       "      <td>1.832773e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2021-03-02</td>\n",
       "      <td>4.259235</td>\n",
       "      <td>5.704739</td>\n",
       "      <td>17227.064066</td>\n",
       "      <td>24796.476169</td>\n",
       "      <td>68858.330569</td>\n",
       "      <td>3534.595876</td>\n",
       "      <td>1.810894e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>2021-03-09</td>\n",
       "      <td>4.772121</td>\n",
       "      <td>6.198393</td>\n",
       "      <td>18268.585842</td>\n",
       "      <td>28877.567833</td>\n",
       "      <td>78943.134306</td>\n",
       "      <td>3513.075745</td>\n",
       "      <td>1.792829e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>2021-03-16</td>\n",
       "      <td>5.345978</td>\n",
       "      <td>6.551620</td>\n",
       "      <td>19706.546866</td>\n",
       "      <td>33068.754927</td>\n",
       "      <td>90582.444099</td>\n",
       "      <td>3489.990522</td>\n",
       "      <td>1.766018e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>2021-03-23</td>\n",
       "      <td>5.825665</td>\n",
       "      <td>6.726284</td>\n",
       "      <td>20868.354599</td>\n",
       "      <td>34819.133831</td>\n",
       "      <td>100768.739416</td>\n",
       "      <td>3477.687299</td>\n",
       "      <td>1.739864e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2021-03-30</td>\n",
       "      <td>5.995909</td>\n",
       "      <td>6.601132</td>\n",
       "      <td>20726.445709</td>\n",
       "      <td>33609.739905</td>\n",
       "      <td>105414.072082</td>\n",
       "      <td>3487.797004</td>\n",
       "      <td>1.739581e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>2021-04-06</td>\n",
       "      <td>5.723745</td>\n",
       "      <td>6.072481</td>\n",
       "      <td>18766.327954</td>\n",
       "      <td>30116.294370</td>\n",
       "      <td>101081.210564</td>\n",
       "      <td>3491.607064</td>\n",
       "      <td>1.754498e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>2021-04-13</td>\n",
       "      <td>5.523819</td>\n",
       "      <td>5.745318</td>\n",
       "      <td>16821.766951</td>\n",
       "      <td>28363.626989</td>\n",
       "      <td>95450.420813</td>\n",
       "      <td>3492.218493</td>\n",
       "      <td>1.754715e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>2021-04-20</td>\n",
       "      <td>5.449153</td>\n",
       "      <td>5.649627</td>\n",
       "      <td>15495.438429</td>\n",
       "      <td>27708.322501</td>\n",
       "      <td>91212.480944</td>\n",
       "      <td>3501.864917</td>\n",
       "      <td>1.745771e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>2021-04-27</td>\n",
       "      <td>5.570456</td>\n",
       "      <td>5.863615</td>\n",
       "      <td>14835.044441</td>\n",
       "      <td>26813.379265</td>\n",
       "      <td>90690.350737</td>\n",
       "      <td>3504.816405</td>\n",
       "      <td>1.720940e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>2021-05-04</td>\n",
       "      <td>5.601265</td>\n",
       "      <td>6.000551</td>\n",
       "      <td>13904.033819</td>\n",
       "      <td>24561.368440</td>\n",
       "      <td>89710.534337</td>\n",
       "      <td>3494.827975</td>\n",
       "      <td>1.684229e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>2021-05-11</td>\n",
       "      <td>5.380745</td>\n",
       "      <td>5.805802</td>\n",
       "      <td>12126.761932</td>\n",
       "      <td>20104.314882</td>\n",
       "      <td>83474.846287</td>\n",
       "      <td>3456.169006</td>\n",
       "      <td>1.629007e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>2021-05-18</td>\n",
       "      <td>4.849405</td>\n",
       "      <td>5.254680</td>\n",
       "      <td>9638.561432</td>\n",
       "      <td>14424.572926</td>\n",
       "      <td>71112.485398</td>\n",
       "      <td>3417.275755</td>\n",
       "      <td>1.566887e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>2021-05-25</td>\n",
       "      <td>4.258407</td>\n",
       "      <td>4.653505</td>\n",
       "      <td>7170.828449</td>\n",
       "      <td>9890.580250</td>\n",
       "      <td>57844.126097</td>\n",
       "      <td>3377.540010</td>\n",
       "      <td>1.523612e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>2021-06-01</td>\n",
       "      <td>3.678226</td>\n",
       "      <td>4.062641</td>\n",
       "      <td>5221.782298</td>\n",
       "      <td>7039.982553</td>\n",
       "      <td>46071.273892</td>\n",
       "      <td>3337.848824</td>\n",
       "      <td>1.484148e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>2021-06-08</td>\n",
       "      <td>3.201160</td>\n",
       "      <td>3.567064</td>\n",
       "      <td>4067.364679</td>\n",
       "      <td>5718.023843</td>\n",
       "      <td>37832.624917</td>\n",
       "      <td>3294.872415</td>\n",
       "      <td>1.439670e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>2021-06-15</td>\n",
       "      <td>2.714641</td>\n",
       "      <td>3.075546</td>\n",
       "      <td>3149.567733</td>\n",
       "      <td>4227.802997</td>\n",
       "      <td>30278.163775</td>\n",
       "      <td>3239.496315</td>\n",
       "      <td>1.390818e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>2021-06-22</td>\n",
       "      <td>2.166506</td>\n",
       "      <td>2.510758</td>\n",
       "      <td>2089.121511</td>\n",
       "      <td>2164.549343</td>\n",
       "      <td>22088.471385</td>\n",
       "      <td>3181.159999</td>\n",
       "      <td>1.317185e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>2021-06-29</td>\n",
       "      <td>1.628706</td>\n",
       "      <td>1.899248</td>\n",
       "      <td>1164.661817</td>\n",
       "      <td>469.308462</td>\n",
       "      <td>15069.857015</td>\n",
       "      <td>3133.789205</td>\n",
       "      <td>1.253520e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>2021-07-06</td>\n",
       "      <td>1.155281</td>\n",
       "      <td>1.318703</td>\n",
       "      <td>456.058152</td>\n",
       "      <td>-849.421374</td>\n",
       "      <td>9308.579545</td>\n",
       "      <td>3107.641211</td>\n",
       "      <td>1.199926e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>2021-07-13</td>\n",
       "      <td>0.785731</td>\n",
       "      <td>0.860348</td>\n",
       "      <td>0.568072</td>\n",
       "      <td>-1727.343010</td>\n",
       "      <td>5164.320293</td>\n",
       "      <td>3078.653358</td>\n",
       "      <td>1.170062e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>2021-07-20</td>\n",
       "      <td>0.555837</td>\n",
       "      <td>0.592325</td>\n",
       "      <td>-103.794163</td>\n",
       "      <td>-1766.483087</td>\n",
       "      <td>3042.336423</td>\n",
       "      <td>3063.932442</td>\n",
       "      <td>1.176924e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>2021-07-27</td>\n",
       "      <td>0.357325</td>\n",
       "      <td>0.353936</td>\n",
       "      <td>-131.383990</td>\n",
       "      <td>-1299.873652</td>\n",
       "      <td>1501.923929</td>\n",
       "      <td>3061.778775</td>\n",
       "      <td>1.198950e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>2021-08-03</td>\n",
       "      <td>0.264329</td>\n",
       "      <td>0.232104</td>\n",
       "      <td>94.367543</td>\n",
       "      <td>-461.147426</td>\n",
       "      <td>1776.416227</td>\n",
       "      <td>3071.228817</td>\n",
       "      <td>1.234750e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>2021-08-10</td>\n",
       "      <td>0.252918</td>\n",
       "      <td>0.202202</td>\n",
       "      <td>393.785391</td>\n",
       "      <td>236.775270</td>\n",
       "      <td>2958.777657</td>\n",
       "      <td>3098.257204</td>\n",
       "      <td>1.271416e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>2021-08-17</td>\n",
       "      <td>0.308432</td>\n",
       "      <td>0.270190</td>\n",
       "      <td>522.895148</td>\n",
       "      <td>398.831267</td>\n",
       "      <td>3993.215780</td>\n",
       "      <td>3126.045054</td>\n",
       "      <td>1.286108e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>2021-08-24</td>\n",
       "      <td>0.351958</td>\n",
       "      <td>0.332750</td>\n",
       "      <td>514.549771</td>\n",
       "      <td>255.883855</td>\n",
       "      <td>4335.253657</td>\n",
       "      <td>3146.962255</td>\n",
       "      <td>1.308007e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
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      ],
      "text/plain": [
       "         date  % WEIGHTED ILI  %UNWEIGHTED ILI       AGE 0-4      AGE 5-24  \\\n",
       "0  2020-07-07        1.411405         1.591912   2702.484841   3332.729258   \n",
       "1  2020-07-14        1.158573         1.290422   2218.531072   2579.458693   \n",
       "2  2020-07-21        0.976981         1.071673   2071.280693   2982.152038   \n",
       "3  2020-07-28        0.864371         0.942170   2075.758784   3694.024325   \n",
       "4  2020-08-04        0.842937         0.925972   2110.089350   4409.159154   \n",
       "5  2020-08-11        0.830736         0.907552   2196.719302   5443.275954   \n",
       "6  2020-08-18        0.824093         0.910349   2374.147513   6513.932715   \n",
       "7  2020-08-25        0.838693         0.988355   2342.311507   6335.618813   \n",
       "8  2020-09-01        0.857786         1.100826   2174.361215   5172.548823   \n",
       "9  2020-09-08        0.897425         1.191296   2298.593304   4835.880476   \n",
       "10 2020-09-15        0.929066         1.277326   2403.423011   4836.815652   \n",
       "11 2020-09-22        1.005786         1.451736   2366.960229   4946.945534   \n",
       "12 2020-09-29        1.052060         1.624004   2154.857600   4641.439955   \n",
       "13 2020-10-06        0.952962         1.521194   2011.039889   4609.024371   \n",
       "14 2020-10-13        0.775218         1.249543   1913.729149   4564.133866   \n",
       "15 2020-10-20        0.621133         0.971701   1941.265439   5010.430969   \n",
       "16 2020-10-27        0.479081         0.731611   2135.351552   5452.592733   \n",
       "17 2020-11-03        0.377170         0.575832   2262.961799   5483.482491   \n",
       "18 2020-11-10        0.329128         0.489898   2577.009487   6049.818211   \n",
       "19 2020-11-17        0.409451         0.549002   3170.148406   7986.377921   \n",
       "20 2020-11-24        0.576136         0.736760   3871.406976  10577.198365   \n",
       "21 2020-12-01        0.750226         0.936262   4419.460440  12819.594867   \n",
       "22 2020-12-08        0.901229         1.095970   4992.888899  13501.629240   \n",
       "23 2020-12-15        1.093728         1.283268   5540.935185  13499.067230   \n",
       "24 2020-12-22        1.294767         1.484959   6309.568185  14148.514800   \n",
       "25 2020-12-29        1.473360         1.727058   7112.021938  15853.113699   \n",
       "26 2021-01-05        1.735037         2.132600   8317.052175  18584.748635   \n",
       "27 2021-01-12        1.964189         2.525607   9188.190908  20427.971685   \n",
       "28 2021-01-19        2.119312         2.739661  10084.885565  21021.533196   \n",
       "29 2021-01-26        2.280924         2.860568  11464.390012  21103.179251   \n",
       "30 2021-02-02        2.512907         3.056032  13033.252807  21090.914177   \n",
       "31 2021-02-09        2.860601         3.520307  14393.599437  20978.455100   \n",
       "32 2021-02-16        3.350611         4.291435  15698.116188  21661.968840   \n",
       "33 2021-02-23        3.795318         5.040864  16393.904659  22417.901640   \n",
       "34 2021-03-02        4.259235         5.704739  17227.064066  24796.476169   \n",
       "35 2021-03-09        4.772121         6.198393  18268.585842  28877.567833   \n",
       "36 2021-03-16        5.345978         6.551620  19706.546866  33068.754927   \n",
       "37 2021-03-23        5.825665         6.726284  20868.354599  34819.133831   \n",
       "38 2021-03-30        5.995909         6.601132  20726.445709  33609.739905   \n",
       "39 2021-04-06        5.723745         6.072481  18766.327954  30116.294370   \n",
       "40 2021-04-13        5.523819         5.745318  16821.766951  28363.626989   \n",
       "41 2021-04-20        5.449153         5.649627  15495.438429  27708.322501   \n",
       "42 2021-04-27        5.570456         5.863615  14835.044441  26813.379265   \n",
       "43 2021-05-04        5.601265         6.000551  13904.033819  24561.368440   \n",
       "44 2021-05-11        5.380745         5.805802  12126.761932  20104.314882   \n",
       "45 2021-05-18        4.849405         5.254680   9638.561432  14424.572926   \n",
       "46 2021-05-25        4.258407         4.653505   7170.828449   9890.580250   \n",
       "47 2021-06-01        3.678226         4.062641   5221.782298   7039.982553   \n",
       "48 2021-06-08        3.201160         3.567064   4067.364679   5718.023843   \n",
       "49 2021-06-15        2.714641         3.075546   3149.567733   4227.802997   \n",
       "50 2021-06-22        2.166506         2.510758   2089.121511   2164.549343   \n",
       "51 2021-06-29        1.628706         1.899248   1164.661817    469.308462   \n",
       "52 2021-07-06        1.155281         1.318703    456.058152   -849.421374   \n",
       "53 2021-07-13        0.785731         0.860348      0.568072  -1727.343010   \n",
       "54 2021-07-20        0.555837         0.592325   -103.794163  -1766.483087   \n",
       "55 2021-07-27        0.357325         0.353936   -131.383990  -1299.873652   \n",
       "56 2021-08-03        0.264329         0.232104     94.367543   -461.147426   \n",
       "57 2021-08-10        0.252918         0.202202    393.785391    236.775270   \n",
       "58 2021-08-17        0.308432         0.270190    522.895148    398.831267   \n",
       "59 2021-08-24        0.351958         0.332750    514.549771    255.883855   \n",
       "\n",
       "         ILITOTAL  NUM. OF PROVIDERS            OT  \n",
       "0    17439.662198        3080.320316  1.342750e+06  \n",
       "1    14030.595373        3066.143876  1.326612e+06  \n",
       "2    12263.867698        3060.660311  1.342087e+06  \n",
       "3    11775.541865        3049.166623  1.364134e+06  \n",
       "4    12181.050698        3030.611186  1.386885e+06  \n",
       "5    12223.428181        3005.961789  1.401025e+06  \n",
       "6    11808.576963        2985.465491  1.395366e+06  \n",
       "7    11544.068963        2967.074970  1.399139e+06  \n",
       "8    11660.298057        2940.809491  1.386037e+06  \n",
       "9    12828.926618        2923.562815  1.386792e+06  \n",
       "10   13519.800538        2889.795275  1.382223e+06  \n",
       "11   14913.654047        2876.090613  1.384362e+06  \n",
       "12   15767.764470        2854.507203  1.374647e+06  \n",
       "13   15220.053510        2833.764565  1.377211e+06  \n",
       "14   13353.708030        2814.412226  1.366981e+06  \n",
       "15   11887.493575        2801.313365  1.373712e+06  \n",
       "16   10948.119749        2804.629482  1.396905e+06  \n",
       "17   10580.607087        2806.798882  1.447871e+06  \n",
       "18   10677.091744        2806.715276  1.501868e+06  \n",
       "19   12751.867479        2822.747823  1.578970e+06  \n",
       "20   15642.055438        2834.115298  1.631351e+06  \n",
       "21   18592.547582        2842.341104  1.677509e+06  \n",
       "22   20888.320806        2857.210742  1.714638e+06  \n",
       "23   23528.232899        2890.156000  1.772600e+06  \n",
       "24   26269.423621        2991.217331  1.869280e+06  \n",
       "25   28846.811324        3171.841339  1.973652e+06  \n",
       "26   32443.341275        3345.058722  2.056525e+06  \n",
       "27   35072.670969        3462.316597  2.074185e+06  \n",
       "28   36649.356658        3511.999428  2.059325e+06  \n",
       "29   39314.352275        3535.432169  2.032153e+06  \n",
       "30   43351.407937        3550.155152  1.997569e+06  \n",
       "31   48585.487142        3559.303154  1.948657e+06  \n",
       "32   55116.724284        3560.076972  1.888864e+06  \n",
       "33   60966.216939        3546.928039  1.832773e+06  \n",
       "34   68858.330569        3534.595876  1.810894e+06  \n",
       "35   78943.134306        3513.075745  1.792829e+06  \n",
       "36   90582.444099        3489.990522  1.766018e+06  \n",
       "37  100768.739416        3477.687299  1.739864e+06  \n",
       "38  105414.072082        3487.797004  1.739581e+06  \n",
       "39  101081.210564        3491.607064  1.754498e+06  \n",
       "40   95450.420813        3492.218493  1.754715e+06  \n",
       "41   91212.480944        3501.864917  1.745771e+06  \n",
       "42   90690.350737        3504.816405  1.720940e+06  \n",
       "43   89710.534337        3494.827975  1.684229e+06  \n",
       "44   83474.846287        3456.169006  1.629007e+06  \n",
       "45   71112.485398        3417.275755  1.566887e+06  \n",
       "46   57844.126097        3377.540010  1.523612e+06  \n",
       "47   46071.273892        3337.848824  1.484148e+06  \n",
       "48   37832.624917        3294.872415  1.439670e+06  \n",
       "49   30278.163775        3239.496315  1.390818e+06  \n",
       "50   22088.471385        3181.159999  1.317185e+06  \n",
       "51   15069.857015        3133.789205  1.253520e+06  \n",
       "52    9308.579545        3107.641211  1.199926e+06  \n",
       "53    5164.320293        3078.653358  1.170062e+06  \n",
       "54    3042.336423        3063.932442  1.176924e+06  \n",
       "55    1501.923929        3061.778775  1.198950e+06  \n",
       "56    1776.416227        3071.228817  1.234750e+06  \n",
       "57    2958.777657        3098.257204  1.271416e+06  \n",
       "58    3993.215780        3126.045054  1.286108e+06  \n",
       "59    4335.253657        3146.962255  1.308007e+06  "
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_scaled_preds, *_ = learn.get_X_preds(new_X)\n",
    "\n",
    "new_scaled_preds = to_np(new_scaled_preds).swapaxes(1,2).reshape(-1, len(y_vars))\n",
    "dates = pd.date_range(start=fcst_date, periods=fcst_horizon + 1, freq='7D')[1:]\n",
    "preds_df = pd.DataFrame(dates, columns=[datetime_col])\n",
    "preds_df.loc[:, y_vars] = new_scaled_preds\n",
    "preds_df = learn.inverse_transform(preds_df)\n",
    "preds_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0f5d0b4",
   "metadata": {},
   "source": [
    "# Conclusion ✅"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cfff1b5c",
   "metadata": {},
   "source": [
    "In this notebook we have covered the following topics:\n",
    "\n",
    "* PatchTST: a new state-of-the-art transformer for long-term multivariate time series forecasting.\n",
    "* how to prepare data for a time series task.\n",
    "* how to use PatchTST within the tsai framework.\n",
    "* how to use predict multiple variables and multiple steps into the future.\n",
    "* how to visualize predictions and compare them to true values.\n",
    "\n",
    "I hope you've found this helpful. Now it's your opportunity to start creating your own forecasts!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c70302bb",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "python3",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
